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COMMUTER EXPOSURE MODEL
User's Guide
by
P.B. Simmon
R.M. Patterson
Atmospheric Science Center
SRI International
Menlo Park, California 94025
Contract 68-02-2981
Project Officer
William Petersen
Meteorology and Assessment Division
Environmental Sciences Research Laboratory
Research Triangle Park, North Carolina 27711
ENVIRONMENTAL SCIENCES RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
RESEARCH TRIANGLE PARK, NORTH CAROLINA 27711
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DISCLAIMER
This report has been reviewed by the Environmental Protection Agency and
approved for publication. Approval does not signify that the contents neces-
sarily reflect the views and policies of the U.S. Environmental Protection
Agency, nor does mention of trade names or commercial products constitute en-
dorsement for use.
11
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ABSTRACT
A topic of increasing concern to planning and regulatory agencies
and to air quality modelers is the high air pollutant levels observed
on roadways. Commuters, who drive during heavy demand periods and are
exposed to perhaps the highest pollutant levels, are the group most
affected. To investigate this problem, the U.S. EPA commissioned a study
to develop a commuter exposure modeling methodology. The methodology has
been designed to compute commuter exposure statistics through simulation
of the traffic, vehicular emissions, and atmospheric dispersion of
roadway-related air pollutants. A numerical computer-oriented code,
based on the commuter exposure methodology, has been developed and imple-
mented and is described in the User's Guide.
The commuter exposure modeling package consists of two programs that
are to be run separately on the computer. The first program is an emis-
sions preprocessor, which has been separated from the main model to facili-
tate updating of the model package when currently accepted methods for
computing emissions factors are revised by EPA. The second program is
the main portion of the commuter exposure model, which simulates traffic
flow, computes the emissions rates resulting from the traffic (using the
emission factors calculated by the preprocessor), simulates the dispersive
effects of the atmosphere, and computes statistics describing commuter
exposure.
The commuter exposure model is a tool that can be used to assess
the pollutant levels to which commuters are exposed in various metropolitan
areas. Since the model treats the spatial variation of exposure, regions
iii
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of the city in which commuters experience high exposures can be identi-
fied from model output. If a single commute pathway is of interest, that
pathway can be examined in detail. The model facilitates study of expo-
sure levels in relation to the percentage of the commuting population
exposed. The length of time of exposure is also readily available for
use in health effects studies. In addition to being useful for determin-
ing absolute exposure levels, the model can also assess the effects of
implementing roadway improvements or transportation control measures.
The commuter exposure modeling package consists of two documents.
This volume is the User's Guide. It describes program execution and
provides the user with the information needed to run the program.
The other volume provides a detailed description of the model methodology
and code.
This report was submitted in partial fulfillment of Contract No.
68-02-2981 by SRI International under the sponsorship of the U.S. En-
vironmental Protection Agency. This report covers the period October
1978 through April 1981.
iv
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CONTENTS
Abstract iii
Figures vii
Tables vif-f
1. Introduction 1
2. Program Usage 5
3. Brief Description of Model Methodologies 7
Overview 7
Traffic Modeling 8
- Uninterrupted Flow 9
- Freeway Backup 9
- Interrupted Flow 10
Emission Rate Computations 12
Dispersion Modeling 14
Dispersion of Pathway Emissions 14
- Dispersion of Nonpathway Sources 17
On-Roadway/In-Vehicle Concentration Relationship . . 19
Generation of Commuter Exposure Statistics 19
4. Computer Requirements 21
General 21
Program Operation 22
Commuter Exposure Programs 22
- Core Storage Requirements 23
Job Runstream 23
Program Structure 27
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- Emission Factor Preprocessor 27
- Commuter Exposure Model 30
Module Description 36
- Emission Factor Preprocessor Subroutines ... 37
- Commuter Exposure Model Subroutines 38
5. Data Specifications 45
Input Data Requirements 45
- General 45
- Emission Factor Preprocessor (PREPRS) Input
Data 59
- Commuter Exposure Model (CEMAP) Input Data . . 62
Preparation of Input Data 71.
- Definition of the Modeling Approach 71
- Data Sources 83
- Additional Recommendations 87
Model Output 93
- Description of Output 93
- Uses and Interpretation of Output 99
- Model Limitations 102
6. Sample Application 107
Overview 107
Data Sources 107
Input Data Formulation 109
Discussion of Results of Sample Application Ill
References 129
vi
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FIGURES
1 Sample CDC and UNIVAC Runstreams for Program PREPRS 25
2 Sample CDC and UNIVAC Runstreams for Program CEMAP 26
3 Flowchart of Emissions PreprocessorMain Program 31
4 Flowchart of Main Control Module of Main Portion
of Commuter Exposure Model 32
5 Sample Commuter Pathway Network With Grid Overlay 80
6 Service Volume of a Signalized Intersection Approach .... 91
7 Typical Relationships Between V/C Ratio and Average
Overall Travel Speed, in One Director of Travel, On
Urban and Suburban Arterial Streets 92
8 Operating Speed Related to Level of Service, Route
Type Volume/Capacity Ratio and Ring 92
vii
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TABLES |g*£
1 Checklist of Steps for Application of the Commuter ifr';';;f
Exposure Package 24 FX.-'S'
'" *£->'
2 Subroutines of the Commuter Exposure Package 28 !;' X\<
3 Input Data Requirements for the Commuter Exposure I'\.,H-'
Model Package 46 C ;;T"
4 Definition of Value Intervals of Meteorological >,
Parameters 69 .. ?:,
5 Numbers Associated with Combinations of ;.;" -:-
Meteorological Parameters 72 '
=:*
6 Locale Types 83
7 Examples of Weekday Diurnal Traffic Cycles on >
Freeways in the San Francisco Bay Area 88
8 Examples of Weekday Diurnal Traffic Cycles on Non-
freeway Streets in the San Francisco Bay Area 89
9 Commuter Exposure Model Output 94
10 Meteorological Data for St. Louis Sample Applica- ;. ,
tion Day13 May 1977, Hour 0900 Ill .'"';'
viii
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SECTION 1
INTRODUCTION
Those concerned with air quality assessment are increasingly recog-
nizing the inadequacy of fixed point monitoring data for characterizing
the pollutant exposure of various population groups. The spatial varia-
tion of concentrations over short distances is recognized to be suffi-
ciently great for some pollutants that concentration measurements made
at fixed locations are not necessarily representative of the concen-
trations to which people, as moving receptors, are exposed. Since the
objective of air quality regulation in general is to protect the health
of people, the air quality assessment community has realized that simu-
lation modeling should be directed toward modeling pollutant concentra-
tions at the locations where people spend time, considering the amount:
of time spent at each location. Thus, it is being recognised that the
quantity of real concern is not concentration, but rather exposure, which
implies the interaction of concentration and a (human) receptor. The
population group experienc ing the highest potential exposure to auto-
mobile-related pollutants is commuters. To assess commuter exposures,
the U.S. Environmental Protection Agency commissioned a study (Simmon
and Patterson, 1979) to develop methodologies for modeling commuter ex-
posure using both computer and manual techniques. A numerical computer-
oriented code, based on the commuter exposure methodology formulated in
the referenced study, has been developed and implemented and is described
in this User's Guide.
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The commuter exposure model is a tool that can be used to assess the
pollutant levels to which commuters are exposed in various metropolitan
areas. The model can be used to look at annual average exposures when it
is run in what is called the annual mode; when the user wants to examine
specific or high risk worst-case commutes, the model can be run in the
short-term mode. The model output provides the user with a substantive
description of commuter exposure. Since the model treats the spatial
variation of exposure, regions of the city in which commuters experience
high exposures can be identified from model output. If a single commute
pathway is of interest, that pathway can be examined in detail. The model
facilitates study of exposure levels in relation to the percentage of the
commuting population exposed. The length of time of exposure is also -
readily available for use in health effects studies. In addition to being
useful for determining absolute exposure levels, the model can also assess
the effects of implementing roadway improvements or transportation control
measures.
The commuter exposure modeling package consists of two programs that
are to be run separately on the computer. The first program is an emissions
preprocessor, which has been separated from the main model to facilitate
updating of the model package when currently accepted methods for computing
'emissions factors are revised by EPA. The second program is the main por-
tion of the commuter exposure model, which simulates traffic flow, computes
the emissions rates resulting from the traffic (using the emission factors
calculated by the preprocessor), simulates the dispersive effects of the
atmosphere, and computes statistics describing commuter exposure.
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This User's Guide to the commuter exposure modeling package describes
program execution and provides the user with the information needed to run
the program. The other volume gives detailed descriptions of the model
methodology and a listing (on microfiche) of the FORTRAN code. In this
guide, the potential uses of the model are discussed and a brief overview
of the modeling methodology is presented. A comprehensive section describ-
ing the details of the implementation of the model methodology on a computer
and the associated computer requirements is included. The data necessary
to run the model are fully described and numerous recommendations are made
for preparing the input data. Model output is described and the uses and
interpretation of the output are discussed along with the limitations of
the model. Finally, a sample application is presented with a more detailed
description in an Appendix.
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SECTION 2
PROGRAM USAGE
The commuter exposure model is meant to be just what its name
implies, a model that will calculate the exposure of commuters to
air pollution and express this exposure in a variety of temporal and
spatial formats. In no sense is it intended to function as an analytical
simulation model for any of the three modules that it includes; i.e.,
traffic, emissions, and dispersion.
The commuter exposure model is a tool that can be used to assess
the pollutant levels to which commuters are exposed. The model can
generate data on annual average exposures or on exposures during spe-
cific commutes. Since the model treats the spatial variation of expo-
sure, regions of an urban area in which commuters experience high expo-
sures can be identified using the model. The model facilitates the
study of exposure levels in relation to the percentage of the commuting
population exposed to different levels. The length of time of exposure
is also readily available for use in health effect studies.
In addition to determining absolute exposure levels, the model can
also be used to assess the effects of implementing roadway improvements,
transportation control measures, changes in vehicle mix or emission con-
trols, or even the effects of topographical changes. The differences in
commuter exposure under various planning alternatives can be found by
running the model with and without the proposed changes and comparing
the computed results.
Every attempt has been made to make the commuter exposure model
usable by the technical person who is experienced in either dispersion
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modeling or traffic modeling. However, instances may arise in which
technical judgment must be applied by the user, and for these cases it
is recommended that the user seek technical assistance from someone
working in the relevant field. Examples include such things as the best
way to treat traffic flow at a signalized intersection under the constrants
of the model formulations, and whether to treat a particular roadway as
a street canyon or not for dispersion modeling purposes.
In examining the output of the model, consideration must be given
to the assumptions made in defining the parthway network from the data
base. The more thorough and reliable the input data, the more reliable
will be the results. Model output can be no better than the quality of
the input data. If input parameters were estimated in a number of
instances, note should be taken as to the potential effects of output
values. As with any simulation model of this type, its best application
is in examining relative differences rather than absolute values.
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SECTION 3
BRIEF DESCRIPTION OF MODEL METHODOLOGY
3.1. Overview
The crux of the commuter exposure modeling problem is defining the
modeling area, and the most critical aspect in defining the modeling
area is choosing the appropriate commuter pathways. The commuter ex-
posures that are calculated and the statistics that are derived all
depend directly on the pathways that are defined.
For the problem to be manageable, a reasonable number of major com-
muting routes or pathways having the highest numbers of vehicle miles
of travel (VMT) by commuters must be defined. While it is recognized
that these pathways will not carry all commuters, they will include the
commuters at risk of experiencing high exposures. The commuters "missed"
will, on the average, be traveling shorter times and distances on less
heavily traveled roads.
Commuting trips are not begun (for a morning commute) on the commute
pathways as defined here, but rather, on local surface streets and "col-
lectors." To handle the exposure during the approach to and departure
from the pathway, minor pathways are used that are representative of
the travel to and from the route. Likewise, at the end of a (morning)
commute, ending routes are specified that are representative of travel
from the major pathways to work locations. The process is reversed for
the evening commute.
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Traffic on roadways other than pathways Is treated by allocating
VMT on a grid square basis (see Ludwig, et al., 1977). This approxima-
tion is made because the major contribution to pollutant concentrations
on a pathway is made by the vehicles traveling on the pathway itself.
The gridded street network is divided into the primary network., which
includes roadways for which traffic volumes are available, and the secondary
network, which includes the remaining roadways. The VMT on the secondary
network are assumed to be a function of the primary traffic and locale.
The gridded traffic data are supplied by the user.
The model is designed to be used in a short-term mode simulating
a single commute period, or in an annual mode that simulates an average
commute over a year. A number of data are generated for each mode of
operation.
3.2 Traffic.' Modeling
The traffic module of the commuter exposure model handles two separ-
ate traffic flow regimes: uninterrupted flow and interrupted flow. Travel
on expressways is generally uninterrupted, although during a backup, the
flow can become severely constrained to the point of becoming "stop and
go." Interrupted flow describes travel on arterials with traffic signals
at the intersections. (Intersections that have stop signs are not
considered since they will not be present for the main traffic flow on
an arterial, commuter pathway.) The following discussion details those
elements that describe the traffic flow.
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3 .2.1. Uninterrupted Flow
The characteristics of uninterrupted flow can be obtained from two
parameters: Demand volume (V) and free-flow capacity (C) of the road
segment. Volume data in some form are required. Data in the form of
average daily traffic (ADT) or annual average daily traffic (AADT) are
converted to hourly values through diurnal and seasonal distributions
either by the user or by the model using national average distributions.
Travel speed is important for estimating vehicle pollutant emissions
along the pathways and for calculating travel time, which is then used
in the exposure and dose calculations. Speed may be input by the user,
or if it is not, the model calculates speed on each pathway segment as
a function of volume, capacity, and the type of roadway.
3.2.2. Freeway Backup
When demand exceeds capacity a traffic backup may occur. For the
purposes of commuter exposure modeling a simple model was developed to
estimate the size of a backup and the average delay that it would cause.
It was assumed that the rate of growth of the backup equals the differ-
ence in demand minus capacity. The total number of vehicles affected
is then
A
» ' T-/7 a)
A
where N is the total number affected while demand exceeds capacity and
q and s are the demand and capacity in vehicles per unit time. The ex-
pression is evaluated after demand falls below capacity. The average
delay (D) is calculated by
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vN , (2)
where k is the distance between vehicles, v is the travel speed, and
k/v is the time headway.
3.2.3. Interrupted Flow
Interrupted flow pertains to traffic conditions when movement is
routinely stopped, or interrupted, for a finite period of time. For
the commuter exposure model, only two causes of interrupted flow need
be considered: signalized intersections and toll booths, where traffic
goes through mode changes from cruise to deceleration, idle, acceleration,
and back to cruise. Emissions (and hence concentrations) and times of
exposure are influenced by these mode changes.
Because of vehicle emission characteristics, the average route
speed methodology used for freeways is used for arterials outside of
the central business district (CBD).
A more thorough analysis is warranted to CBD pathway segments and
toll booths. Calculations must be made of driving mode changes, the
proportion of vehicles changing modes, and the length of time spent in
the different modes. Basically, this requires a calculation of queue
lengths and delay at intersections. The parameters required are demand
volume, capacity, and the traffic signal parameters of cycle length and
the length of the green phase. The user may choose to input the signal
parameters or have the model calculate them. Queue length and delay
data can then be sued to calculate modal emissions.
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Once capacity, volume and the signal parameters are known, the pro-:
portion of vehicles that stop for a signal can be calculated from the
length of the green pahse G, the signal cycle length Cy, the hourly
demand volume V, and the capacity service volume per hour of green, Cs.
The number (n) of vehicles subject to queueing delay is
VCy fl-G/Cyl
N 3600 I 1-V/Cs J (3)
while the maximum length of the queue (Lq, m) is
Lq - 8N/M (4)
where 8 is the distance (meters) occupied by each queued vehicle and M
is the number of lanes in the approach. On the average, a stopped
vehicle waits one-half the length of the red phase, so the average delay
to those vehicles is
D - 0.5 (Cy-G) . (5)
For toll booths, the methodology is different because all vehicles
must stop and wait to be served. The average number of vehicles waiting
to leave a toll booth is computed from classical queueing theory as
T/
N " C^V . (6)
The queue length at a toll booth is given by Equation 4, while the aver-
age delay for vehicles at a toll booth is:
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The traffic modeling methodology used in this model has been de-
monstrated for air pollution work in a number of studies (e.g., Patterson,
1975). While there are other more complicated methods of handling traffic
flow modeling, the present approach has been found to be quite suitable
for air quality simulation. The basic outputs of the traffic module
speed, volume, travle time, and modal behaviorare those required by
the emissions and dispersion modules to calculate commuter exposure.
3.3. Emission Rate Computations
Emission rates for each pathway segment and grid square are computed
using one of two types of emission treatments: a treatment based on the
average route speed and one that considers the effects of driving mode
changes on emissions. The first treatment is the EPA methodology (EPA,
1978) based on the Federal Test Procedure (FTP). The second treatment
is the "Automobile Exhaust Emission Modal Analysis Model," EPA (1974),
or modal model. Emissions modeling along pathways requires both treat-
ments; FTP-based emissions estimates are suitable for nonpathway sources.
Emission rates (Q) for pathway segments that are freeways, express-
ways, or arterials outside the central business district (CBD) are found
by multiplying an emission density, E, computed using the FTP methodology
and chosen according to the average route speed on the segment, by the
demand volume (V) on the segment. To compute the average emission rate
over a segment having congested flow, it is convenient to break the emissions
into two components: Those occurring during normal flow, E, and the
excess over normal flow emissions that occur during congested flow, E'-E,
where E1 represents the emissions during congested flow. The component
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of total emissions due to uncongested flow is given by Q, with E chosen
according to the average route speed on the uncongested portion of the
segment. The average emission rate over the pathway segment is the sum
of the components due to uncongested (Q) and congested (Q1) flow, as
given by:
C Lq Q1 m EV 4- (E'-E)N Lg/L (8)
3600 L (1609)(3600)
where E1 corresponds to an average route speed of 20 mi/h, L * segment
length (m), and T is the duration (seconds) of the backup.
Travel on arterials within the CBD is characterized by interrupted
flow and low speeds, and a modal emissions treatment should be used.
This is available through an adaptation of EFA's modal model.
For the commuter exposure model, simulation of the effects of modal
emissions is facilitated through the introduction of the concept of
excess modal emissions. Excess modal emissions are those that occur over
and above those that would have occurred had the vehicle not stopped. The
total excess emissions, Eg(g/m/s), the sum of idle emissions and the
acceleration and deceleration parts of the excess emission, £««, may be
expressed as
where b,g constant (g/s) and P » proportion of vehicles stopped for the
signal. The cruise emissions that would have occurred had the vehicle
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not stopped are found by integrating the modal model expression for the
steady-speed emission rate over 1/2T. Once emissions are calculated
with the modal treatment, they are averaged for each pathway segment.
3.4. Dispersion Modeling
3.4.1. Dispersion of Pathway Emissions
For the purpose of computing the dispersion of vehicular emissions
on commute pathways, liaited-access and nonlimited-access pathway seg-
ments are distinguished from segments located in a street canyon.
The dispersion of pollutants emitted by vehicles on both limited-
access and nonliaited-access roadways is computed using a technique taken
from the CALINE3 (Benson,. 1979) dispersion model. In this formulation
individual roadway links are divided into a series of elements that each
contribute to the concentration at a receptor. As the distance between
element and receptor increases, the size of the element increases as a
function of wind/roadway angle to gain efficiency in computation. The
concentrations resulting from the individual elements are summed to form
a total concentration estimate at a receptor.
Each element is modeled as an "equivalent" finite line source that
is centered at the midpoint of the element and positioned normal to the
wind direction vector. The length of the equivalent line is a function
of element size and wind/roadway angle. Emissions are assumed to dis-
perse in a Gaussian distribution downwind of the element. To arrive at
an equitable distribution of emissions, each element is divided into five
sub-elements whose source strengths assume a step-function form.
The Gaussian crosswind finite line source equation is approximated
in the formulation by the following equation:
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X -
1
u
i-1
5
i
*
-2- 7
SG2 * ^
CUT
2
exp
r-(Z+2*k*L)'
2 SGZ
i
(10)
where n = total number of elements, CNT = number of multiple reflections
required for convergence, U = wind speed, L - mixing height, SGZ. = verti-
cal dispersion function for ith element, QE. = central sub-element lineal
source strength for ith element, WT. = source strength weighting factor
for jth sub-element, and PD.. = normal probability density function for
jth sub-element of ith element. PD.. is calculated by use of a fifth
order polynomial approximation.
CALINE3 treats the region directly over the roadway as a mixing zone
in which there are uniform emissions and turbulence. Initial vertical
dispersion is formulated as a function of mixing zone residence time and
roadway half-width and is used to account for the thermally and mechanically
enhanced dispersion over and downwind of the roadway arising from the
passage of the vehicles.
Dispersion on a roadway with tall buildings on both sides is greatly
influenced by the presence of the buildings. The street-canyon dispersion
treatment used in the commuter exposure model is based on the empirical
street-canyon model developed by Johnson et al. and modified by Ludwig
and Dabberdt. Their studies give evidence of a helical air circulation
in street canyons, with concentrations differing on the windward and lee-
ward sides of the street. The concentration arising from street-canyon
emissions on the leeward side of buildings is given by:
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U+O. 5
where X * concentration on leeward side of street (g/m ), Q = line
source emission rate (g/m/sec), K * empirically derived nondimensional
constant-7, U wind speed (m/sec), x and z - horizontal and vertical
distance of the receptor relative to the center of the traffic lane (m),
and LO m dimension representing vehicle width 2: 2 m. The concentration
on the windward side of the street is given by:
KQ, (H-z)
X_ *
w W(U+0.5)H
where X » concentration on windward side of street (g/m ) , H = average
building height (m), and W * street width (m).
In the commuter exposure model, the concentration on the roadway
is assumed to be the average of the concentration on the windward and
leeward sides of the street. Since the receptor is on the roadway, x
and z are zero.
Substituting, and combining the above equations gives:
2(U-K3.5)lLQ W
(11)
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Segment pollutant concentrations are summed for each pathway and
integrated over travel time to yield pathway exposures.
3.4.2. Dispersion of Nonpathway Source Emissions
It is expected that the portion of the total pathway integrated
concentration (exposure) that results from nonpathway sources will be
small in comparison to the portion resulting from traffic on the commuter
pathways themselves. Therefore, a very simple emissions and dispersion
treatment is used.
The line-source emissions on nonpathways have been aggregated into
area-source emissions from grid squares. For each pathway segment a
concentration is computed according to the so-called Hanna (1971) disper-
sion treatment, as discussed by Simmon and Patterson (1979). The basic
assumption of the treatment is that the area source strength in a metro-
politan area varies by less than a factor of 10 over distances of a few
kilometers. That assumption permits the horizontal cross-wind dispersion
component to be neglected in comparison with the vertical component and
concentration can be approximated in the following manner :
CQ
Y - o
X ' U
where
C
V"
a(l-b)
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D » a parameter related to city size, and a and b are constants repre-
sentative of various stability regimes. (See Simmon and Patterson, 1979)
Thus, for any point on a pathway, this equation can be used to compute
the concentration from area sources. The commuter model computes these
concentrations on a pathway-by-pathway basis. For a given pathway, the
model takes the first segment and determines which grid squares contain
the segment end points and midpoint. For each of these points, a con-
centration normalized by wind speed is calculated according to the
relationship discussed above. The traffic module will have generated
the times at which the average vehicle reaches the midpoint and the
endpoint of the segment. The normalized integrated concentration over
the segment (the exposure) is given by:
EU
X1UT1
+ X2UT2 1 '
where EU = normalized exposure, X, U » normalized concentration at end-
point 1 of the segment, X..U » normalized concentration at the segment
midpoint, X£U = normalized concentration at endpoint 2 of the segment,
T. « travel time from endpoint 1 to midpoint, and T« * travel time
from midpoint to endpoint 2. This normalized exposure applies to only
one segment of a pathway. The model computes similar exposures for all
segments of a pathway, and sums the results to yield the normalized
pathway exposure resulting from non-pathway sources.
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3.5 On-Roadway/In-Vehicle Concentration Relationship
The little information that exists about the relationship between
the concentration on the roadway and the concentration within a vehicle
indicates that concentrations of CO inside a vehicle are about equal to
that on the outside. Therefore, the commuter exposure model assumes the
concentration at the two locations are identical.
3.6 Generation of Commuter Exposure Statistics
Exposures are computed for each pathway according to the meteorologi-
cal conditions of the mode of model operation chosen by the model user.
If the short-term mode is chosen, one exposure is computed for each path-
way, for the input meteorological conditions and traffic information.
If the model is operating in the annual mode, morning and evening exp-
sures are computed for each pathway for 480 sets of meteorological condi-
tions (each combination of 6 wind speeds, 16 wind directions, and 5 atmo-
spheric stability classes). These exposures are weighted according to
the frequency of occurrence of each set of conditions and summed for
each pathway. In addition to the exposures, the model stores the total
travel time or times on each pathway and the average number of commuters
using the pathway.
When the model is run in the short-term mode, the output includes a
list of the exposure from pathway sources, exposure from non-pathway
sources, total exposure, travel time, and average concentration on each
pathway for the meteorological and traffic conditions that have been input
to the model. Also output are the average and standard deviation of pathway
exposure. When the model is in the annual mode it lists the annual average
morning and evening pathway, non-pathway, and total exposure, travel times, and
19
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average concentrations on each pathway as well as the annual average
exposures over the modeled region. For either mode, the model produces
data for two histograms. For the short-term mode the data pertain to
a single commute; for the annual mode, the data are representative of
annual variations. The histogram data include the percentage of the
commuting poplulation treated by the model in each of several exposure
classes, and the probability of experiencing the exposure levels in
each of several classes (i.e., the percentage of time commuters are ex-
posed to the levels of the exposure class). For the annual mode subsets
of these statistics may be output at the user's discretion. For a
more detailed discussion of how the statistics are computed, see Section
5.3.1. Finally, the model user has the option of attaching a plotting
program to call for model output in graphical form.
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SECTION 4
COMPUTER REQUIREMENTS
4.1 General
The numerical commuter exposure model code has been designed to be
as modular as possible to facilitate understanding of the code; to reduce
the possibility of coding error and ease debugging; and to increase the
ease with which modifications to the code can be made in the future.
Liberal use of comments ensures that program logic and technique are
easily understood. The code was written in FORTRAN IV, and every attempt
was made to make the program as machine-independent as possible. Thus,
the code should be easily adaptable for use on various makes of computer.
In the past, techniques for estimating mobile source emissions have
been subject to frequent change. A number of updates to the emissions
modeling methodologies have been issued, and it is anticipated that other
updates will be forthcoming as additional vechicle test data become avail-
able. The model has therefore been designed in two parts: an emissions
preprocessor and the main model. Separation of the Federal Test Procedure
(FTP) emissions modeling and the modal emissions modeling from the main
commuter exposure model will allow modification of the emissions treat-
ments of the commuter exposure model package, as emissions updates are
issued, without change to the main model. Whenever changes, however minor,
are made to a large computer program, the possibility of error is consider-
ably greater than when modification is made to a small program. Thus,
changing the emissions preprocessor is much simpler and less subject to
error than changing the main model. Modification to the main model would
21
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be required only if the mobile source emissions modeling methodologies
underwent major methodological change.
The following sections detail the manner in which the commuter pro-
gram is to be operated, discuss the structure of the computer program,
and describe the major modules of the program.
4.2 Program Operation
4.2.1. Commuter Exposure Programs
The commuter exposure modeling package consists of two separate
computer programs: the emission factor preprocessor, PREPRS, and the
commuter exposure model, CEMAP. The preprocessor has its own set of
input data, listed in Section 5.2, and would normally be run before the
main portion of the model. The preprocessor creates disc files (on output
device number 8) containing the tables of emission factors that will be
read and used by the main model. Thus, the preprocessor run must have
been executed before the main model job can be submitted. If the user
decides to exercise the option of inputting pathway and gridded emissions
to the model directly, rather than having the model package compute them,
the emissions preprocessor would not be run, and flags would be set on
input to the main model as a cue that emissions will be input directly.
Once the emissions preprocessor has been run successfully, the main
model, CEMAP, should be executed. The input data for CEMAP are also listed
in Section 5.2.
The input data in card image format is read from input device number
5, while the disc files created by PREPRS are read from device 8. The types
of output data generated by the model are governed in part by the flags the
22
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user has set on card type 8M of the input data (see Section 5.2). All
model output is in printed or graphical form. A checklist of the steps
the user must follow to use the commuter exposure modeling package is
given in Table 1.
4.2.2. Core Storage Requirements
The preprocessing program, PREPRS, requires 110700 (octal) words
(60 bits/word) of core storage, and the main model, CEMAP, requires
117700 (octal) words on a CDC 6400 when provisions for five pathways are
included. These requirements may vary somewhat from one make of computer
to another. Most arrays in the two programs are dimensioned for three
pollutants to facilitate modification of the program if at some time
in the future a user wanted to run more than one pollutant at a time.
If the larger core storage requirements caused by having the arrays di-
mensioned as they are create a problem, the dimensions of some of the
arrays could be reduced so that the pollutant index is one rather than
three. Using the glossary of symbols and Tables 5 and 6 of the other
volume of this package ("Description of Model Methodology and Code"),
such reductions could be made. However, the user is cautioned that numer-
ous DO loops exist in the program to increment pollutant index, and these
loops are set to operate for CO only, from 2 to 2, i.e., DO 10 IP = 2,2.
If the dimensions of arrays containing pollutant as an index are to be
reduced, the loops must be changed to: DO xxx IP = 1,1.
4.2.3. Job Runstream
The emission factor preprocessor, PREPRS, has a simple runstreatn.
Figure 1 shows sample runstreams for the preprocessor for CDC and UNIVAC
23
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Table 1
CHECKLIST OF STEPS FOR APPLICATION OF THE
COMMUTER EXPOSURE PACKAGE
1. Identify commute pathways.
2. Identify pathway segments.
3. Define coordinate system.
4. Define grid squares.
5. Determine segment endpoint and grid square corner coordinates.
6. Collect vehicle registration data.
7. Gather information on cold-starting/hot-starting vehicles and
vehicle air conditioner usage.
8. Identify I/M program specifications.
9. Identify pathway segment street types, volumes, capacities,
physical characteristics, and speeds or determine emission rates,
10. Identify intersection/freeway backup/toll booth specifications.
11. Determine acceleration/deceleration rates.
12. Determine diurnal traffic distributions.
13. Determine primary to secondary street VMT ratio by locale.
14. Determine number of commuters on each pathway.
15. Determine VMT on primary streets and locale of each grid square
or grid square emission rate.
16. Compute morning and evening joint frequency distributions of
wind speed, wind direction, and stability (annual mode).
17. Gather morning and evening average ambient air temperatures
(annual mode).
18. Gather wind speed, wind direction, stability, and ambient air
temperature of commute of interest (short-term mode).
19. Run PREPRS (unless pathway and grid square emissions are to be
user-supplied).
20. Run CEMAP.
24
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CDC Runs trearn
PAW1,P30,T100,CM15000.
ACCOUNT (PBPAW,PSWORD)
GET.PREPRS.
FTN, I=PREPRS, R»2.
MAP(FULL)
GET,TAPE5=PREIN.
DEFINE,TAPE8=PREOUT.
LGO.
UNIVAC Runstream
(3RUN -
@ASC,A FILE.
-------
' CDC Runstream
PAW2,P30,T100, CM15000.
ACCOUNT(PBPAW,PSWORD)
GET.CEMAP.
FTN,I=CEMAP,R=2.
MAP(FULL)
GET,TAPE5=CEMIN.
GET,TAPE8=PREOUT.
LGO.
UNIVAC Runstream
@RUN -
@ASG,A FILE.
(3ASC,A FILES.
(§USE 8. , FILES.
@XQT FILE.CEMAP
Input data cards
(?FREE 8.
@FIN
Figure 2 Sample CDC and UNIVAC Runstreams for
Program CEMAP
26
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machines. The program PREPRS is contained in a disc file called PREPRS.
The input data for the program, in card image format (card types IP
through 6P), are in file PREIN. They are read from device number 5.
The output of the preprocessor is written on a disc file called PREOUT
on device number 8.
The main model, CEMAP, also has a simple runstream. Sample CDC
and UNIVAC runstrearns for CEMAP are given in Figure 2. The program CEMAP
is contained in a disc file called CEMAP. The input data cards (card
types 1M through 9M) are on a file called CEMIN and are read from device
number 5. The output of the preprocessor, file PREOUT, is read from
device number 8.
4.3 Program Structure
4.3.1. Emission Factor Preprocessor
The emission factor preprocessor comprises a short main program, 5
subroutines that were written for the commuter exposure model, and 13
subroutines and BLOCK DATA that are from EPA's MOBILE1 (U.S. EPA, 1978)
computer program for the computation of mobile source emission factors.
All of the MOBILE1 program has been incorporated into PREPRS with the
exception of subroutine OUTPUT. The subroutine now called OUTPUT was
written for the preprocessor. Major changes were made to the MOBILE1
subroutines INPUT and the main program (now called subroutine MOBLE1).
Very minor changes were made to subroutine EFCALX and ALUH. No other
routines were modified.
To facilitate future updates of the commuter exposure model, the
original MOBILE1 statements that were to be removed for this application
27
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TABLE 2. SUBROUTINES OF THE COMMUTER EXPOSURE PACKAGE
Portion of
Model Package
and Subroutine
Name
Short Description
Calling
Subroutine
Function
emissions
preprocessor
INPT
FTP
CORFAC
MODMOD
OUTPUT
Main model
INDATA
TRAF
FREE
ART
MODE
Main control program of
the emissions preprocessor
Module that reads and
edits input data
FTP emissions module
Module chat computes
correction factors
Modal emissions module
Output module for
emission factors
Main control program of
the main model
Module that reads and
edits input data
Control module for
traffic computations
Traffic computation
module for freeways
Traffic computation
module for arterials
Traffic computation
module for
CBD locations
Main program
of emissions
preprocessor
Main program
of emissions
preprocessor
Main program
of emissions
preprocessor
Main program
of emissions
preprocessor
Main program
of emissions
preprocessor
Main program
of main model
Main program
of main model
TRAF
TRAF
TRAF
Controls flow through emissions pre-
processor program
Reads and edits all input data for the
emissions preprocessor
Computes emission factors (using MOBILE1)
for non-CBD arterials, freeways, and
expressways, and factors for grid squares
Computes temperature, cold-starts,
air conditioning, vehicle type
distribution correction factors
for modal emissions
Computes tables of cruise and acceleration/
deceleration emission factors
Prints all emission factor tables and
writes them on files
Controls flow through the main portion of
the commuter exposure model package
Reads and edits all input data for the
main portion of the model
Controls traffic data computations.
Returns volume, speed, driving mode, and
travel time data for emission and exposure
computations
Computes volume, speed, and driving time
data for emission and exposure computations
for freeway segments
Computes volume, speed, and driving time
data for emission and exposure computations
for arterial segments
Computes volume, speed, mode, and driving
time data for emission and exposure
computations for CBD segments
-------
TABLE 2. (concluded)
Portion of
Model Package
and Subroutine
Name
Short Description
Calling
Subroutine
Function
Main model
(continued)
EHISS
KPATHE
DISPRS
ROAD
STREET
NFATHD
SHORTY
STATIS
ANHOUT
BLOCK DATA
GRAF
Control nodule for
emissions computations
Nbnpathvay source
emissions module
Control module for dis-
persion computations
Dispersion module for
roadways that are not
street canyons
Dispersion module for
street canyon
Nonpathway source dis-
persion module
Output module for short-
term statistics
Annual statistical data
generation module
Output module for annual
statistics
Stores data
Module through which
graphical output may
be created
Main program
of main model
Main program
of main model
Main program
of main model
DISPRS
DISPRS
DISPRS
Main program
of main model
Main program
of main model
Main program
of main model
Main program
of main model
Computes emission rate on each pathway
segment using results of traffic modules,
and emissions preprocessor
Computes emission rate in each grid square
resulting from nonpathway sources
Controls computation of dispersion of path-
way source emissions according to whether
a pathway is or is not a street canyon and
dispersion on non-pathway source emissions.
Computes normalized concentrations on non-
street-canyon pathway segments for various
meteorological conditions during the morn-
ing and evening commutes or for the meteoro-
logical conditions specified for the short-
term mode
Computes exposures on street-canyon pathway
segments for either the annual or short-
term mode of operation
Computes normalized exposures on each
pathway from non-pathway sources
Computes and prints model output for
short-term mode of operation
Computes annual average histogram data
Prints annual statistics for annual mode
of operation
Stores data used in more than one
subroutine
Contains appropriate common statements
for user to attach code that will cause
histogram data and other output to be
plotted graphically
29
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were made into comments. Thus, it will be clearer which statements were
changed, deleted or added when a new version of MOBILE1 is to be similarly
incorporated.
Table 2 lists the subroutines in the emissions preprocessor, except
for subroutines that will be called from the modified MOBILE1 emissions
program. The table gives the name of the subroutine, a short description,
the name of the subroutine that calls it, and the function of the routine.
The main emission factor preprocessor program will first read the
basic variables required for its operation. Next, it will call subroutine
FTP, which in turn will call the MOBILE1 subroutines to compute an emis-
sion factor table for use by the main model in computing emissions on
pathways that are freeways, expressways, or arterials outside the Central
Business District (CBD), and emissions from nonpathway sources. The main
program then calls subroutine CORFAC to compute factors that will correct
modal emissions for temperature, cold-starts, air conditioner usage, and
vehicle type distribution. CORFAC computes these factors using some of
the MOBILE1 subroutines. Next, the main program of PREPRS calls sub-
routine MODMOD to compute modal emission factor tables for use by the main
model in computing emissions on CBD arterials. Finally, subroutine OUTPUT
is called to print the output of the preprocessor and write the emission
factor table files. Figure 3 is a flow chart of the main program of the
emissions preprocessor. It graphically illustrates the flow of control
through the program that was described above.
4.3.2. Commuter Exposure Model
Table 2 lists all of the subroutines of the commuter exposure model.
Figure 4 is a flow chart of the main control module of the main portion
30
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PROGRAM PREPRS
CALL SUBROUTINE INPT TO
READ BASIC VARIABLES
CALL SUBROUTINE FTP TO COMPUTE FTP
EMISSION FACTOR TABLE; FTP
CALLS MOB)LEI SUBROUTINES
CALL SUBROUTINE
CORFAC TO COMPUTE CORRECTION FACTORS
CALL SUBROUTINE MOOMOO TO COMPUTE MOOAL
EMISSION FACTOR TABLE
FIGURE 3 FLOWCHART OF EMISSIONS PREPROCESSOR - MAIN PROGRAM
31
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c
PROGRAM CEMAP
CALL INOATA TO READ
AND EDIT INPUT DATA
CALL TRAFF. WHICH CONTROLS TRAFFIC
COMPUTATIONS; TRAFF CALLS SEVERAL
TRAFFIC SUBROUTINES
PATHWAY
EMISSIONS READ
DIRECTLY?
YES
CALL EMISS TO COMPUTE EMISSION
RATE ON EACH PATHWAY SEGMENT
CALL EMISS TO COMPUTE
ROADWAY WIDTH ONLY
CALL NPATHE TO COMPUTE NONPATHWAY
EMISSIONS IN EACH GRID SQUARE
CALL OISPRS. WHICH CONTROLS DISPERSION
COMPUTATIONS: OISPRS CALLS NPATHO
TO COMPUTE CONCENTRATIONS ON PATHWAYS
FROM NONPATHWAY SOURCES AND SEVERAL
DISPERSION SUBROUTINES
CALL STATIS TO COMPUTE
ANNUAL HISTOGRAM DATA
CALL SHORTY TO COMPUTE AND PRINT
MODEL OUTPUT FOR SHORT-TERM MODE
CALL ANNOUT TO PRINT
ANNUAL STATISTICS
CALL GRAF TO GENERATE
GRAPHICAL OUTPUT
C
END
FIGURE 4 FLOWCHART OF MAIN CONTROL MODULE OF MAIN PORTION
OF COMMUTER EXPOSURE MODEL
32
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of the commuter exposure model. The figure shows how control passes
through the program and indicates the order in which the main subroutines
are called. The first subroutine called is INDATA, which reads and edits
the input data.
The three main modules of the model that treat traffic, emissions,
and dispersion, as well as the subroutines called by the main modules,
are discussed in detail below. A description is also given of the modules
that generate the commuter exposure statistics.
4.3.2.1. Traffic Subroutines
The control module for all pathway traffic computations is subroutine
TRAFF. TRAFF identifies the pathway, the segment on the pathway, the
direction of traffic flow on the segment, and the time of interest. At
this point, TRAFF calls one of three other subroutines: FREE, ART, or
MODE, depending on whether the segment is part of a freeway, an arterial,
or a CBD street. TRAFF then continues its control function for additional
hours, directions, segments, and pathways.
Subroutine FREE checks the form of the traffic data inputs for the
segment and calls other subroutines based on the results. If ADT or AADT
are input, rather than hourly traffic volume data, FREE converts to a
volume for the time of interest. If the speed is not input, the subroutine
checks whether capacity is input. If not, FREE computes the segment
capacity, based on physical and traffic characteristics. A comparison
of volume and capacity is made; if capacity is greater than volume, speed
is calculated as a function of volume and capacity. If volume exceeds
capacity, subroutine FREE handles the freeway backup. If speed were input
33
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initially, FREE skips the capacity and speed calculations and computes
driving time on the segment based on the supplied data.
Subroutine ART performs the same functions as FREE, but uses differ-
ent algorithms that consider characteristics of arterial flow rather than
those of freeways.
Subroutine MODE is called to handle CBD locations. However, MODE
departs from the procedures of FREE and ART and checks whether the traffic
signal parameters for the segment are available. If not, MODE calculates
these parameters based on opposing critical approach volume and roadway
physical data. MODE then calculates queue lengths, delay times, and
driving times in the different modes on the segment.
4.3.2.2. Emission Subroutines
The emissions preprocessor generates tables of emission factors to
be used in calculating the emission rate on each pathway segment and in
each grid square. Computation of an emission rate or rates for each
pathway segment is accomplished by subroutine EMISS. These emission
rates are saved for each segment. If pathway emissions are read directly,
EMISS is not called to compute roadway width and the remainder of the
subroutine is bypassed.
The computation of an emission rate or rates for each grid square is
made by subroutine NPATHE. Subroutine NPATHE is called by the main pro-
gram whether grid square emission rates are to be computed by the model
or read directly.
34
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4.3.2.3 Dispersion Subroutines
Once the traffic and emissions modules have been run, the main con-
trol module of the main portion of the model will call subroutine DISPRS.
This subroutine controls all dispersion computations made by the model.
For the annual mode of model operation, DISPRS begins with the first
segment of the first pathway and checks as to whether the segment is or is
not in a street canyon. If the segment is a street canyon, DISPRS calls
subroutine STREET to make the appropriate dispersion computations for
various meteorological conditions for the morning and evening emission
rates. If the segment is not a street canyon, DISPRS calls subroutine
ROAD to compute normalized concentrations corresponding to various meteoro-
logical conditions and morning and evening emissions. DIPRS continues
to call the appropriate dispersion subroutines until all pathway segments
have been treated.
If the model is in the short-term mode of operation, DISPRS also
begins with the first segment of the first pathway and checks to see if
it is a street canyon. If so, DISPRS calls subroutine STREET to compute
the exposure for the input meteorological conditions. If the segment is
not a street canyon, DISPRS calls subroutine ROAD to compute the concen-
tration on the roadway for the input conditions. As for the annual mode,
DISPRS continues to call the appropriate dispersion subroutines until all
pathway segments have been treated.
Finally, for either mode of model operation, DISPRS calls subroutine
NPATHD to make dispersion computations of emissions from nonpathway sources.
35
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4.3.2.4 Generation of Commuter Exposure Statistics
Once the dispersion computations have been made, the model is ready
to compute commuter exposure statistics in either the short-term or annual
mode of model operation.
If the model is running in the short-term mode, the main control
module calls subroutine SHORTY to compute and print the model output.
When the model is in the annual mode of operation, the main control
module calls two subroutines. First, STATIS is called to compute the
model output of annual statistics. If any of the optional outputs were
requested with input data flags, STATIS also computes the optional out-
puts. Next, subroutine ANNOUT is called to print the annual statistics
and any optional statistics that were requested.
Finally, if graphical output has been called for with a flag on model
input, subroutine GRAF will be called to generate the graphical output.
The software for subroutine GRAF needs to be supplied by the user (see
4.4.2.17).
4.4 Module Description
In the previous sections the overall structures of both the emission
factor preprocessor and the main model were discussed. The flow of control
through the subroutines was outlined and brief mention was made of the
function of the routines. In this section the function of each individual
subroutine is described in somewhat greater detail.
36
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4.4.1. Emission Factor Preprocessor Subroutines
4.4.1.1. Main Program
The main program of the emission factor preprocessor controls the
calling sequence of the preprocessor subroutines. It calls subroutines
INPT, FTP, CORFAC,. and MODMOD and uses common block PRE2.
4.4.1.2 Subroutine INPT
This routine reads and edits the input data needed by the emission
factor preprocessor. It does not call any subroutines and uses common
blocks PRE1, PRE2, PRE3, PRE4, PRE5, and REGCOM.
4.4.1.3 Subroutine FTP
Subroutine FTP calls the former MOBILE1 program several times to
create tables of FTP emission factors. These tables of emission factors
will be used by the main model to compute emission rates on non-CBD path-
ways and from non-pathway sources. FTP calls subroutines MOBLE1 and
OUTPUT; it uses common blocks PRE1, PRE2, PRE4, and PRE6.
4.4.1.4 Subroutine CORFAC
This subroutine computes the temperature/cold start correction
factor ratios needed for model emission factor computations. It also
computes the ratio of total emissions to LDV emissions and air condition-
ing correction factor for use in correction modal emissions. It calls
subroutines INPUT, EFCALX, ALUH, and OUTPUT and uses common blocks MODEM,
LNKCOM, RET1, PRE1, PRE2, PRE5, PRE6, and PRE7.
4.4.1.5 Subroutine MODMOD
This subroutine computes tables of modal excess and cruise emission
factors for use by the main model. It calls subroutine OUTPUT and uses
common blocks MYMCOM, MODEM, PRE2, PRE4, PRE6, and PRE7.
37
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4.4.1.6 Subroutine OUTPUT
This subroutine replaces the output subroutine that was part of MOBILE1.
It prints and writes files of the emission and correction factors computed
by the preprocessor. OUTPUT does not call any other subroutines and uses
common blocks FLGCOM, MYMCOM, MODEM, LNKCOM, IMCOM, REGCOM, PRE1, PRE2,
PRE3, PRE5, PRE6, and PRE7. .
4.4.1.7 Subroutine MOBLE1
Subroutine MOBLE1 was the main program of EPA's MOBILE1 code. It
has been extensively modified and it controls FTP emission factor compu-
tations. It calls several subroutines from EPA's MOBILE1 program: INPUT,
INITEX, LDVIMX, and EFCALX. It uses common blocks PRE1, PRE3, PRE6,
PRE7, FLGCOM, REGCOM, LNKCOM, and ALTCOM.
4.4.1.8 Other Subroutines in PREPRS
The remaining subroutines were taken from EPA's MOBILE1 program.
They are INPUT, EFCALX, BIGCFX, TRKOPC, SPFCLX, INITEX, TFCALX, EFALTX,
BEFGEN, GETCUM, CCEVAX, LDVIMX, and ALUH. There is also a BLOCK DATA
included. Subroutine INPUT has been extensively modified and subroutines
EFCALX and ALUH have been slightly modified. No other subroutines were
modified.
4.4.2 Commuter Exposure Model Subroutines
4.4.2.1 Main Program
The main program of the commuter exposure model controls the calling
sequence of the major modules of the model. The main program calls basi-
cally four types of modules: those controlling traffic, emission rate,
dispersion, and statistical computations. The subroutines called are
38
-------
INDATA, TRAF, EMISS, NPATHE, DISPRS, STATIS, ANNOUT, SHORTY, and GRAF.
The common blocks used in the main program are RUNTYP, OPTION, and
DEFINE.
4.4.2.2 Subroutine INDATA
This subroutine reads all of the input data required by the model
and performs some editing of the data. The routine reads both the card
image data (types IM through 9M) called for in this guide and the disc
files of emission factors created by the preprocessor. INDATA calls no
other subroutines and uses common blocks 10, GENRL, RUNTYP, DEFINE,
PACOOR, SECOND, GRID, META, METST, OPTION, EMSFAC, PAEMIS, TRAFEM, TRAFOT,
SPREAD, TOLL, BAKUP, SIGNAL, INFO, and NOCOMM.
4.4.2.3 Subroutine TRAF
This subroutine controls the traffic module and provides the traffic-
related data required by the emissions and dispersion modules. It selec-
tively calls subroutines FREE, ART, and MODE and uses common blocks
RUNTYPE, TRAFEM, DEFINE, GENRL, TRAFIN, TRAFOT, PACOOR, SPREAD, INFO,
TIMES, SECOND, TOLL, EAKUP, and SIGNAL.
4.4.2.4 Sub rout ine FREE
Subroutine FREE calculates needed traffic parameters for free-flow
movement on freeways. It also handles freeway backup and toll booths.
FREE does not call any other subroutines and uses common blocks RUNTYP,
TRAFEM, DEFINE, GENRL, TOLL, BAKUP, TRAFIN, TRAFOT, PACOOR, INFO, SPREAD,
SECOND, and TIMES.
4.4.2.5 Subroutine ART
This subroutine handles the calculations required for traffic on
arterials. It calls no other subroutines and uses common blocks RUNTYP,
39
-------
TRAFEM, DEFINE, GENRL, TRAFIN, TRAFOT, PACOOR, SPREAD, INFO, SECOND, and
TIMES.
4.4.2.6 Subroutine MODE
This subroutine treats the modal behavior of traffic at signalized
intersections, and it computes signal parameters as required. It calls
no other subroutines and uses common blocks RUNTYP, TRAFEM, DEFINE, GENRL,
TRAFOT, TRAFIN, PACOOR, SIGNAL, INFO, SECOND, and TIMES.
4.4.2.7 Subroutine EMISS
This subroutine computes the emission rate on each path segment using
the emission factors computed by the preprocessor. Emission factors com-
puted with the MOBILE1 methodology are used for freeway and non-CBD seg-
ments, while modal emission factors are used on the CBD segments that
are not freeways. EMISS does not call any other subroutines and uses
common blocks RUNTYP, TRAFEM, DEFINE, PAEMIS, EMSFAC, TRAFOT, PACOOR,
INFO, TRAFIN, and GENRL.
4.4.2.8 Subroutine NPATHE
Subroutine NPATHE computes the emission rates of sources that are
not traveling on pathways, It computes emission rates for each grid
square through which a pathway passes according to the amount of traffic
flow on both non-pathway primary and secondary streets in the square.
NPATHE does not call any other subroutines and uses common blocks GENRL,
RUNTYP, DEFINE, GRID, SECOND, GREMIS, TIMES, and EMSFAC.
40
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4.4.2.9 Subroutine DISPRS
This subroutine controls all of the dispersion computations. It
calls the appropriate routine to compute dispersion of pathway emissions
on each segment according to whether the segment is or is not located
in a street canyon. DISPRS also calls the subroutine that computes the
contribution to the concentration on the pathway that arises from non-
pathway sources. It calls subroutines ROAD, STREET, and NPATHD and uses
common blocks TRAFIN, LSTSEG, DEFINE, RUNTYP, TIMES, NOSTRT, STRT AND METST,
4.4.2.10 Subroutine ROAD
Subroutine ROAD uses the CALINE3 methodology to compute normalized
concentrations on non-street canyon pathway segments. Much of the code
in ROAD is an adaptation of the CALINE3 code. If the model is in the
short-term mode, ROAD computes normalized concentration for the meteoro-
logical conditions input to the model. If the model is in the annual
mode, ROAD computes normalized concentrations for a number of different
combinations of meteorological conditions for both the morning and evening
commutes. It calls no other subroutines and uses common block TRAFIN,
GENRL, 10, PAEMIS, PACOOR, RUNTYP, METST, and META.
4.4.2.11 Subroutine STREET
This subroutine computes normalized exposures (annual mode) or ex-
posure (short-term mode) on pathway segments that are located in street
canyons. It uses an adaptation of the methodology for street canyons
formulated for the APRAC series of models. STREET does not call any other
subroutines and uses common blocks RUNTYP, METST, STRT, LSTSEG, DEFINE,
and PAEMIS.
41
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4.4.2.12 Subroutine NPATHD
This subroutine computes normalized exposures (annual mode) or
exposure (short-term mode) on each pathway segment that arise from
non-pathway sources. The routine determines which grid squares the
segment passes through and uses those emission rates to compute exposure.
NPATHD calls no other subroutines and uses common blocks RUNTYP, METST,
GENRL, DEFINE, GRID, PACOOR, 10, NOPATH, LSTSEG, GREMIS, and TIMES.
4.4.2.13 Subroutine SHORTY
Subroutine SHORTY computes the statistical output of the model when
the model is in the short-term mode. It also prints the output. If the
model is in the annual mode, SHORTY is not called. SHORTY does not call
any other subroutines; it uses common blocks 10, DEFINE, STRT, XOPATH,
NOSTRT, LSTSEG, NOCOMM, SAVE, and TIMES.
4.4.2.14 Subroutine STATIS
This subroutine computes the commuter exposure statistics when the
model is in the annual mode. It is not called when the model is in the
short-term mode. It calls no other subroutines and uses common blocks
DEFINE, META, OPTION, SAVE, STRT, NOPATH, NOSTRT, OPOUT, NOCOMM, PART,
and TIMES.
4.4.2.15 Subroutine ANNOUT
This subroutine prints the model output statistics when the model is
in the annual mode. It is not called if the model is in the short-term
mode. ANNOUT does not call any other subroutines; it uses common blocks
10, DEFINE, OPTION, SAVE, OPOUT, PART, and TIMES.
4.4.2.16 BLOCK DATA
BLOCK DATA stores data that are used in more than one subroutine.
It uses common blocks META and SPREAD.
42
-------
4.4.2.17 Subroutine GRAF
This subroutine does not actually do any plotting. It contains
the common statements that pass the appropriate variables to be plotted,
but, when called, it simply prints a statement that the user must supply
a plotting package. It does not call any other subroutines and, it uses
common blocks RUNTYP, TIMES, 10, OPTION, OPOUT, SAVE, and PART.
43
-------
SECTION 5
DATA SPECIFICATION
5.1 Input Data Requirements
5.1.1 Generail
In this section the input data requirements of the commuter exposure
modeling package are presented. The types of data that are required for
execution of the commuter exposure model are listed and described in
Table 3. The table also contains information on the column number, data
format, FORTRAN symbol, preferred units, data value limits, and typical
values of each input parameter. For information concerning the definition
of the modeling area and the pathway network, the user is referred to
Section 5.2.
Since the commuter exposure model package consists of two parts that
are run independently on the computer, two sets of input data are required:
one for the emissions preprocessor and one for the main model. Card type
numbers with input data for the emissions preprocessor will be followed
by a P, e.g., 2P; card type numbers with input data for the main model
will be followed by an M, e.g., AM.
The data for the emissions preprocessor should be entered on six
types of cards. Type IP contains general information about the simula-
tion to be run. The second card type is optional; it may specify the
vehicle model year and type distributions specific to the city being
modeled. Card type 3P contains information on the. percentages of cold-
45
-------
TABLE 3. INPUT DATA REQUIREMENTS FOR THE COMMUTER EXPOSURE MODEL PACKAGE
(a) Card IP
Column
1-3
4-23
24-29
31
33
35
37
39
41
43
Format
10A2
312
11
I!
11
11
11
11
11
Symbol
(MHEAD(I),
1-1,10)
IDATE
ILOC
MODRUN
LPATH
LNPATH
IMFLAG
MYFLAG
IVTFLG
Units
-
~
"
Value Limits
20 alpha-
numeric
characters
770101-951231
1,2,3
1,2
1,2
1,2
0,1
0,2
0,2
Typical
Value
IP
St. Louis
781215
1
1
1
1
1
2
2
Description
Card type number
Heading (anything user
vants printed at the
beginning of the output)
Date (year, month, day)
City location;
><4000 ft, 2-California,
3->4000 ft
Model mode of operation;
l"annual, 2-short-term
Pathway emissions;
1 -model-calculated ,
2-user-supplied
Nonpathway emissions;
1-model-calculated ,
2"user-supplied
Inspection-maintenance
program;
0-no I/M program,
1-include I/M program
LDV model year distribution;
0-default to national dis-
tribution,
2-user-supplied
Vehicle type distribution;
0-default to national dis-
tribution,
2-user-supplied
(b) Card 2P
1-3
4-63
64-78
20F3.1
5F3.1
F2.1
FMODYR(I),
1-1,20
FVEHTY(J),
J-1,6
~
0. - 1.0
0. - 1.0
2P
0.12
(national
average)
0.76
(national
average)*
Card type number
Fraction of LDV registered,
of vehicle age i,i»l,20
Fraction of total VMT
driven by vehicles of
type j, j-1,6
Omit card type if MYFLAG-IvrFLG-O
*
Default Value
46
-------
TABLE 3. (continued)
(e) Card 3P annual mode
Column
1-3
4-6
7-9
10-12
13-15
16-18
.
19-21
22-24
25-28
29-32
Format
13
13
13
13
13
13
13
74.1
F4.1
Symbol
ICOUN
ICOtDC
IHOTC
ITZMPA
ITEMPP
IACAM
1ACPM
ACCEL
DECEL
Units
*
°T
°r
«l/hr/««c
1/hr/MC
Value Limits
0 - 100
0 - 100
0 - 100
0 - 110
0 - 110
0 - 100
0-100
>0
<0
Typical
Value
3?
20
20
30
SO
60
10
80
2.5
-2.5
Description
Card type number
Percentage of non-catalyst-
equipped LDV VMT accumu-
lated in cold-scare node.
evening commute, on CBO
pathways
Percentage of catalyst-
equipped LDV VMT accumu-
lated in cold-start node.
evening commute, on CBD
pathways
Percentage of catalyst-
equipped LDV VMT accumu-
lated in hot-start mode.
evening commute* on CSD
pathways
Annual average air tempera-
ture during morning com-
mute
Annual average air tempera-
ture during evening com-
mute
Percent of vehicles equip-
ped with air conditioning
that has air conditioner
running during morning
commute
Percent of vehicles equip-
ped with air conditioning
that haa air conditioner
running during evening
commute
Acceleration on CSD path-
ways
Deceleration on CBD path-
ways
Omit if LPATH-2
47
-------
TABLE 3. (continued)
(d) Card 3Pshort-term mode
Column
1-3
4-6
7-9
10-12
13-15
16-18
19-22
23-26
Format
13
13
13
13
13
F4.1
F4.1
Symbol
ICOLDN
ICOLDC
IHOTC
ITEMPS
1ACS
ACCEL
DECEL
Units
_
°F
mi/hr/sec
mi/hr/sec
Value Limits
0 - 100
0 - 100
0 - 100
0 - 110
0 - 100
>0
<0
Typical
Value
3P
20
20
30
73
60
2.5
-2.5
Description
Card type number
Percentage of non-catalyst
equipped LDV VMT accumula-
ted in cold-start mode on
CBD pathways for commute
of interest
Percentage of catalyst-
equipped LDV VMT accumu-
lated in cold-start mode on
CBD pathways for commute
of interest
Percentage of catalyst-
equipped LDV VMT accumu-
lated in hot-start mode on
CBD pathways for commute
ot interest.
Average air temperature
during commute of Interest
Percent of -vehicles equip-
ped with air conditioning
that has air conditioner
running during commute of
interest
Acceleration on CBD pathways
Deceleration on CBD pathways
Omit of LPATH-2
(«) Card 4P annual mode
Column
1-3
4-18
19-33
34-48
Format
513
513
513
Symbol
JCLDNA(L),
L-1,5
JCLDCA(L),
L-1,5
JHDTA(L),
L-1,5
Units
Value Limits
0 - 100
0 - 100
0 - 100
Typical
Value
4P
20
20
10
Description
Card type number
Percentage of non-catalyst
equipped LDV VMT accumu-
lated in cold-start mode
in locale type 1(1-1,5),
morning commute
Percentage of catalyst-
equipped LDV VMT accumu-
lated in cold-start mode
in locale type 1(1-1,5),
morning commute
Percentage of catalyst-
equipped LDV VMT accumu-
lated in hot-start mode
in locale type 1(1-1,5),
morning commute
48
-------
TABLE 3. (continued)
(£} Card 4P short-term mode
1-3
4-18
19-33
34-48
513
513
513
JCOLDN(L) ,
L-1,5
JCOLDC (L) ,
L-1,5
JHOT(L),
L-1,5
0 - 100
0 - 100
0 - 100
4P
20
20
10
Card type number
Percentage of non-catalyst-
equipped LDV VMT accumu-
lated in cold-start mode in
locale type 1(1-1,5) for
commute of interest
Percentage of catalyst-
equipped LDV VMT accumu-
lated in cold-start mode
in locale type 1(1-1,5) for
commute of interest
Percentage of catalyst-
equipped LDV VMT accumu-
lated in hot-start mode in
locale type 1(1-1,5) for
commute of interest
*t
(g) Card 5P annual mode
1-3
4-18
19-33
34-48
513
513
513
JCLDNP(L),
L-1,5
JCLDCP(L),
L-1,5
~
JHOTP(L),
L-1,5
0 - 100
0 - 100
0-100
5P
20
20
10
Card type number
Percentage of non-catalyst
equipped LDV VMT accumu-
lated in cold-start mode
in locale type 1(1-1,5),
evening commute
Percentage of catalyst
equipped LDV VMT accumu-
lated in cold-start mode
in locale type 1(1-1,5),
evening commute
Percentage of catalyst-
equipped LDV VMT accumu-
lated in hot-start mode
in locale type 1(1-1,5),
evening commute
Omit card type if LNPATH-2
For annual mode of operation; no card type 5P for «hort-term mode
(h) Card 6P*
Column
1-3
4-5
6-7
9
10-11
12-13
Format
12
12
11
12
12
Symbol
IMYEAR
IMSTRG
IMMECH
IMBEG
IMEKD
Units
~
"
Value Limts
77-95
10-50
0,1
58-95
59-95
Typical
Value
6P
77
20
0
60
95
Description
Card type number
Year of I/M program imple-
mentation
Stringency level of I/M
program
Mechanic training flag;
0-no
1-yes
Earliest model year included
in I/M program
Latest model year included
in I/M program
Omit If IMFLAG-0
49
-------
TABLE 3. (continued)
(i) Card 1M
Column
1-3
4-23
24-29
31
33
35
37
Format
10A2
312
11
11
11
11
Symbol
(MHEAD(I),
1-1.10)
(H)ATE(I),
1-1.3)
MODRUN
LPATH-
.LNPATH
SPRINT
Units
"
~*~
~-
~
"
Value Limits
20 alpha-
numeric
characters
770101-951231
1.2
1.2.
1,2
1.2
Typical
Value
1M
St. Louis
Run 1
781215
1
1
1
1
Description
Card type number
Heading (anything user
wants printed at the
beginning of the output)
Date (year, month, day)
Model mode of operation;
1-annual, 2-short-term
Pathway emissions;
1 -model-calculated ,
2»user-supplied
Nonpathway emissions;
1 "model-calculated ,
2-user-supplied
Print input data;
1-yes
2-no
0
2M
5
6
400
1.
Card type number
Number of pathways
Number of segments in
pathway i, i-1, NPATHS
Number of grid squares
Converts map units to miles
50
-------
TABLE 3. (continued)
(k) Card 3M~annual node (one card for each segment)
Column
1-3
4-5
6-7
8
9
10-15
16-21
22-27
28-33
34-35
36-37
38-42
43
44
45-46
47-48
49-50
51-52
53-54
55-60
61-66
Format
12
12
11
11
F6.0
F6.0
F6.0
F6.0
12
12
15
11
11
12
12
12
12
12
F6.0
F6.0
Symbol
NPATH
LINK
LINTOP (NPATH , LINK)
LINTTP (NPATH, LINK)
VOLHR (NPATH, LINK, 1)
VOLHR(NPATH,LINK,2)
VOLHR (NPATH, LINK, 3)
VOLHR (NPATH , LINK , 4 )
IVTORM (NPATH, LINK)
IVSPRD (NPATH, LINK)
LINCAP (NPATH, LINK)
NLANE (NPATH , LINK , 1 )
NLANE (NPATH , LINK , 2 )
LANWID (NPATH , LINK)
LINSPD (NPATH, LINK, 1)
LINSPD (NPATH , LINK , 2 )
LINSPD (NPATH , LINK , 3 )
LINSPD (t.TATH , LINK , 4 )
XI (NPATH, LINK)
Yl (NPATH, LINK)
Units
_
veh/hr or
veh/day (if
LPATH-1) or
g/m-sec(if
LPATH-2)
veh/hr or
veh/day (if
LPATH-1) or
g/n-sec (if
LPATH-2)
veh/hr or
veh/day (if
LPATH-1) or
g/m-sec (if
LPATH-2)
veh/hr or
veh/day (if
LPATH-1) or
g/m-sec (if
LPATH-2)
veh/hr
ft
mi/hr
mi/hr
mi/hr
mi/hr
map units
map units
Value Limits
1-5
<20
1,2
1,2,3
>0.
>0.
>0.
>o.
-1 or +1
-1,0, +1
>o
1-6
1-6
9-14
5-60
5-60
5-60
5-60
>0
>0
Typical
Value
3M
5
17
1
2
3600. or
0.0248
1200. or
0.0082
1200. or
0.0082
3600. or
0.0243
-1
+1
1800
2
2
12
30
45
45
30
1550.
1430.
Description
Card type number
Pathway number
Segment or link number
Segment topography;
1-non-street canyon
2-ctreet canyon
Segment type; l-freeway,2-
arterial, 3-surface street
with intersection
Demand volume (if LPATH-1)
or emission rate (if LPATH
-2) in inbound direction,
morning commute
Demand volume (if LPATH-1)
or emission rate (if LPATH
2) in outbound direction.
morning commute.
Demand volume (if LPATH-1)
or emission rate (if LPATH
-2) in inbound direction,
evening commute
Demand volume (if LPATH-1)
or emission rate (if LPATH
-2) in outbound direction.
evening commute
Volume data format; -t-l-per
lane, -1-entire link
Volume data format; -1-ADT,
0-hourly data, +1-AADT
Lane capacity of segment
Number of lanes on segment ,
inbound direction
Number of lanes on segment ,
outbound direction
Lane width
Speed on segment, inbound
direction, morning commute
Speed on segment, outbound
direction, morning commute
Speed on segment, inbound
direction, evening commute
Speed on segment, outbound
direction, evening commute
x-coordinate of first end-
point pf segment
y-coordinate of first end-
point of segment
Omit of LPATH-2
51
-------
TABLE 3. (continued)
(k) Card 3Mannual mode (one card for each segment)
Column
67-72
73-78
79
80
Format
F6.0
F6.0
11
11
Symbol
X2(OTATH,LINK)
Y2(NPATH,LINK)
INTYPE(NPATH.LINK)
INTDAT (NPATH , LINK)
Units
map units
map units
_
_
Value Limits
>0
>0
0,1,2,3
1,2
Typical
Value
1550.
1690.
0
2
Description
x-coordinate of second
endpoint of segment
y-coordinate of second
endpoint of segment
Intersection type; 0-no
intersection; 1-toll booth,
2-signalized, 3-freeway
backup
Intersection data;
1-user-supplied
2-model-calculated
Omit if LPATH-2
flnclude only if IOTYPE-2
52
-------
TABLE 3. (continued)
(1) Card 3Mshort-term mode (one card for each segment)
Column
1-2
4-5
6-7
8
9
10-15
16-21
23
34-35
36-37
38-42
43
44
45-46
47-48
49-50
55-60
61-66
67-72
73-78
79
80
Format
12
12
11
11
F6.0
F6.0
11
12
12
15
11
11
12
12
12
F6.0
F6.0
F6.0
F6.0
11
11
Symbol
NPATH
LINK
LINTOP (NPATH, LINK)
LINTYP (NPATH, LINK)
VOLHR (NPATH , LINK , 1 )
VOLHR (NPATH, LINK, 2)
IBODND
IVFORM (NPATH , LINK)
IVSPRD (NPATH, LINK)
LINCAP (NPATH, LINK)
NLANE (NPATH , L INK , 1 )
NLANE (NPATH, LINK, 2)
LANWID (NPATH , LINK)
LINSPD (NPATH, LINK, 1)
LINSPD (NPATH, LINK, 2)
XI (NPATH, LINK)
Tl (NPATH, LINK)
X2 (NPATH, LINK)
Y2 (NPATH, LINK)
INTYPE (NPATH , LINK)
INTDAT (NPATH, LINK)
Units
veh/hr or
veh/day (if
LPATH-1) or
g/m-sec (if
LPATH-2)
veh/hr or
veh/day (if
LPATH-1) or
g/m-sec (if
LPATH-2)
_
veh/hr
ft
mi/hr
mi/hr
map units
map units
map units
map units
Value Limits
1-5
<20
1,2
1,2,3
>0.
>o.
1 or 2
-1 or +1
-1,0,+1
>0
1-6
1-6
9-14
5-60
5-60
>0
>0
>0
>0
0,1,2,3
1,2
Typical
Value
3M
5
17
1
2
3600. or
0.0248
1200. or
0.0082
1
-1
+1
1800
2
2
12
30
45
1550.
1430.
1550.
1690.
0
2
Description
Card type number
Pathway number
Segment or link number
Segment topography; 1-non-
street canyon, 2-street
canyon
Segment type: l-freeway,2-
arterial, 3-surface street
with intersection
Demand volume (if LPATH-1)
or emission rate (if LPATH
-2), inbound direction
Demand volume (if LPATH-1)
or emission rate (if LPATH
-2) , outbound direction
Direction of commute; 1-
inbound , 2-outbound
Volume data format; +l-per
lane, -1-entire link
Volume data format; -1-ADT,
0-hourly data, +1-AADT
Lane capacity of segment
Number of lanes on segment,
inbound direction
Number of lanes on segment,
outbound direction
Lane width
Speed on segment, inbound
direction
Speed on segment, outbound
direction
x-coordinate of first end-
point of segment
y-coordinate of first end-
point of segment
x-coordinate of second end-
point of segment
y-coordinate of second end-
point of segment
Intersection data; 0-no
intersection; 1-toll booth,
2-signalized, 3-freeway
backup
*
Intersection data; 1-user-
supplied , 2-model-calculated
Omit if LPATH-2
Include only if INTYPE-2
53
-------
(m) Card 4M*short-term mode (one card for each segment having INTYPE^O)
Column
1-3
4-5
6-7
8-11
12-15
16-19
20-23
24-27
28-31
32-35
36-39
40-42
43-45
46-48
49-51
52-55
1
56-59
60-63
64-67
68-69
70-71
72-73
74-75
Format
12
12
F4.0
F4.0
F4.0
F4.0
F4.0
F4.0
F4.0
F4.0
F3.0
F3.0
F3.0
F3.0
F4.0
F4.0
F4.0
F4.0
F2.0
F2.0
F2.0
F2.0
Symbol
NPATH
LINK
ADV (NPATH, LINK, 1)
ADV (NPATH, LINK, 2)
ADV (NPATH, LINK, 3)
ADV (NPATH, LINK, 4)
ACAP (NPATH, LINK, 1)
ACAP (NPATH, LINK.2)
ACAP (NPATH, LINK.3)
ACAP (NPATH, LINK, 4)
CYCL(NPATH,LINK,1)
CYCL (NPATH, LINK, 2)
GPH(NPATH,LINK,1)
GPH( NPATH, LINK, 2)
VOLBAK(NPATH.LINK.l)
VOLBAK (NPATH , LINK, 2 )
CAPBAK (NPATH , LINK , 1 )
CAP BAK(NPATH, LINK, 2)
BAKTIM (NPATH , LINK , 1 )
SAKTIM (NPATH , LINK , 2 )
TOLLS (NPATH, LINK, 1)
TOLLS (NPATH, LINK, 2)
Units
veh/hr
veh/hr
veh/hr
veh/hr
veh/hr
of green
veh/hr
of green
veh/hr
of green
veh/hr
of green
sec
sec
sec
sec
veh/hr
veh/hr
veh/hr
veh/hr
min
min
__
Value Limits
1-25
<50
>0.
>0.
>o.
>o.
20.
>0.
>0.
>0.
>sum of
all phases
>sum of
all phases
>o.
20.
>o.
>0.
>o.
>0.
>0.
>o.
>0.
>0.
Typical
Value
4M
5
17
600.
per lane
1200.
per lane
600.
per lane
1200.
per lane
1200.
per lane
1800.
per lane
1200.
per lane
1800.
per lane
90.
90.
15.
45.
2300.
per lane
2300.
per lane
2000.
per lane
2000.
per lane
15.
15.
3
3
Description
Card type number
Pathway number
Segment or link number
'Demand volume on segment
approach if inbound communte
Demand volume on opposing
controlling approach if in-
bound commute
Demand volume on segment
approach if outbound commute
''Demand volume on opposing
controlling approach if
outbound commute
^Capacity of segment control-
ling approach if inbound
commute
Capacity of cross street
controlling approach if
inbound commute
'''Capacity of segment approach
if outbound commute
^"Capacity of cross street
controlling approach if
outbound commute
Cycle length (if inbound com-
mute)
5 Cycle length (if outbound
commute)
Sphase green time (if inbound
commute)
Phase green time (if outbound
commute)
**Demand volume during back-
up, if inbound commute
**Demand volume during back-
up , if outbound commute
**Capacity(if inbound commute)
**Capacity(if outbound commute)
**Averaging time for backup
(if inbound commute)
**Averaging time for backup
(if outbound commute)
^Number of toll booths, in-
bound direction (if inbound
commute)
Number of toll booths, out-
bound direction (if outbound
commute)
tt
Omit if LPATH-2
fEnter only if INTYPE-2
5Enter only if INTYPE-2 and INTDAT-1
*£nter only if INTYPE-3
Enter only if INTYPE-1
Note: Data must be entered in the position(inbound or outbound) appropriate to the
direction of travel of the short-term commute being modeled.
54
-------
TABLE 3. (continued)
(n) Card 5Mannual mode
Co liinm
1-3
4-78
Format
1515
Symbol
(NCOMM(I,1),
I-l.KPATHS)
(NCOMM(I,2),
I-l.NPATHS)
Units
people/commute
people /commute
Value Limits
>0
>0
Typical
Value
5M
20000
20000
Description
Card type number
Number of commuters
on pathway i, morning com-
mute
Number of commuters
on pathway i, evening com-
mute
(o) Card 5Mshort-term mode
1
1-3
4-78
1515
(NCOMM(I.l),
I-l.NPATHS)
people /commute
>0
5M
20000
Card type number
Number of commuters
on pathway i, commute of
interest
55
-------
Table 3(Continued)
(p) Card 6Mannual model
Column
1-3
4-6
7-9
10-34
Format
13
13
5F5.0
Symbol
IAMPC
IPMPC
RATSEC (K),
K-1,5
Units
Value Limits
0 - 100
0 - 100
>o.
Typical
Value
6M
30
32
0.4
Description
Card type number
Percentage of average daily
VMT on primary and second-
ary streets during an aver-
age hour of the morning
commute
Percentage of average daily
VMT on primary and second-
ary street during an average
hour of the evening commute
*Ratio of secondary to pri-
mary VMT for locale type k,
k-1,5
0m±t if LNPATH-2
(q) Card 6Mshort-term mode
1-3
4-6
7-31
13
5F5.0
IPCWH
RATSEC (K) ,
K-1,5
0 - 100
>0.
6M
16
0.4
Card type number
Percentage of daily VMT on
primary and secondary
streets during worst hour
of commute
*Ratio of secondary to pri-
mary VMT for locale type
k, k-1,5
Omit if LNPATH-2
(r) Card 7M
1-3
4-6
7-16
17-26
27-36
37-46
48
49-58
13
F10.0
F10.0
F10.0
F10.0
11
E10.3
LGRID
XG(LGRID)
YG(LGRID)
GSIZE(LGRID)
GVMT (LGRID)
GLOC (LGRID)
GEMIS (LGRID)
map units
map units
map units
veh-mi/day
_
g/m2/day
1 - 500
>0.
>0.
>0.
to-
1,2,3,4,5
>o.
7M
23
1525.
1310.
20.
1
4500.
Card type number
Grid square number
x-coordinate of SW corner
of grid square
y-coordinate of SW corner
of grid square
one side of length of grid
square
*Daily VMT on primary streets
in grid square
*Grid square locale type;
1-CBD, 2-commercial/sub-
urban , 3-residential ,
4-industrial, 5-rural/other
^Grid square average daily
emission rate from primary
network
Omit if LNPATH-2
Omit if LNPATH-1
56
t
-------
Table 3 (Concluded)
(s) Card 8Mannual mode
Column
1- 3
4-63
Format
6F10.0
Symbol
(((FREQA(K.L.M),
K-1,6),L-1,16),
M-1,6)
(((FREQP(K,L,M),
K-1,6),L-1,16),
M-1,6)
Units
Value Limits
0. - 1.0
0. - 1.0
Typical
Value
8M
0.002
0.002
Description
Card type number
Joint frequency of
occurrence of stability k,
wind direction 1, wind
speed m; k-1,6; 1*1,16;
m-1,6 for morning commute
period
Joint frequency of
occurrence of stability k,
wind direction i, wind
speed m; k-1,6; i-1,16;
m-1,6 for evening commute
period
(t) Card 8Mshort-term mode
1- 3
4-13
14-16
18
F10.0
13
11
WS
IWD
ISTAB
__
m sec"
degrees
>1.0
0 - 360
1-5
SM
2.4
120
4
Card type number
Wind speed during worst
hour of commute
Wind direction during
worst hour of commute
Stability during worst
hour of commute
(u) Card 9M (optional output)annual mode
1- 3
4-13
14-23
24-25
26-55
57
512
512
12
1013
11
(NOPT1(M>(
M-1,5)
(NOPT2(M),
M-1,5)
NOPT3
(NMET(N),
N-1,10)
IGRAF
~
1 to no. of
pathways
1 to no. of
pathways
1 to no. of
pathways
1 - 576
0, 1
9M
1, 8, 17
2, 10, 21
10
62, 208,
342, 461
1
Card type number
Up to 5 pathway numbers for
which percentage of com-
muters in each of several
exposure classes will be
printed
Up to 5 pathway numbers for
which probability of ex-
periencing exposure levels
in each of several exposure
classes will be printed
Pathway number for which
exposures for the meteor-
ological conditions given
below will be printed
Up to 10 numbers of sets of
meteorological conditions
for which exposure on path-
way above will be printed
Flag for graphics package;
0-no graphical output,
1-call graphical package
For annual mode of operation; no card type 9M for short-term mode.
57
-------
starting and hot-starting vehicles on CBD pathways as well as ambient air
temperature, acceleration and deceleration rates, and information on ve-
hicle air conditioner usage. Card types 4P and 5P contain information
for non-pathway sources on percentages of cold- and hot-starting vehicles.
Card type 6P, included only if an inspection/maintenance program is in
effect, specifies the details of the I/M program. Of course, if both
pathway and nonpathway emissions are to be read directly, the emissions
preprocessor need not be run and its input data need not be prepared.
The data for the main model should be entered on nine types of cards.
Type 1M contains general information about the computer simulation to be
run, such as a heading, the date, and various run flags. The second card
type tells the model the number of pathways, the number of segments in
each pathway, the number of grid squares that will be treated in the
simulation, and so forth. The next two card types contain traffic infor-
mation about each commute pathway segment. One card type 3M is input for
each segment of each pathway, and one card type 4M is input for each segment
that has an intersection, a toll booth, or a freeway backup. Card type
AM is omitted if pathway emissions are input directly. Card type 6M
records information concerning the vehicle miles traveled on the network
and is required for emissions computations. Information on nonpathway
sources in input on card type 7M (one card for each grid square); meteoro-
logical data appear on card type 8. Card type 9 contains several output
flags that cause additional output of the specific types requested to be
printed.
Card types 3P, 4P, 3M, 4M, 5M, 6M and 8M are shown with two formats
in Table 3: The user selects the format appropriate to the specified
mode of model operation (annual or short-term).
53
-------
5.1.2 Emission Factor Preprocessor (PREPRS) Input Data
Card Type IPGeneral data relating to the operation of the emissions
preprocessor are input on card type IP. The first input is an alphanumeric
heading, up to 20 characters long, that may say anything the user would
like to have printed at the beginning of the preprocessor's output. The
date is entered next on this card type, and then seven flags are set. The
first flag indicates if the city being modeled is. at a low or high alti-
tude or in California. The second flag indicates whether the model is to
be run in the annual or short-term mode. The next two flags indicate
whether pathway and nonpathway emissions are to be model-calculated or
user-supplied. The next flag specifies whether an inspection/maintenance
program is in effect, and the last two flags determine whether the LDV
model year registration distribution and the vehicle type distribution
are to be supplied by the user or the national average distributions are
to be supplied by the model as default parameters.
Card Type 2PIf the distributions of model year and vehicle type
needed to compute mobile source emissions are to be user-supplied, they
are entered on card type 2P. Otherwise, this card type is omitted. The
fraction of the annual travel by each of 20 model years, beginning with
the calendar year being modeled and followed by the 19 previous model
years, is entered on this card. These fractions must sum to 1.0. Finally,
the fraction of the annual travel driven by light-duty vehicles, 2 classes
of light-duty trucks, heavy-duty gasoline-powered trucks, heavy-duty
diesel trucks, and motorcycles will be input. These fractions must also
sum to 1.0. Either or both of the distributions may be included, depend-
ing on how the flags on card type 1M were set.
59
-------
Card Type 3PThe type of data to be entered on card type 3P depends
on the mode of operation of the model. A description of the data required
for both the annual and short-term modes of operation is given in Table 3.
For the annual mode, the model requires the entry of the annual aver-
age percentages of cold-starting LDV that are not catalyst-equipped, that
are catalyst-equipped, and that are hot-starting and catalyst-equipped
on CBD pathways for the evening commute. Commuting vehicles usually
begin the evening commute in the CBD; therefore, the percentage should
refer to the CBD pathways. If pathway emissions are to be read directly
(LPATH=2) these percentages may be omitted. The next data inputs on
card type 3P are the annual average ambient air temperatures for the
morning and evening commute periods, followed by the percentage of vehicles
equipped with air conditioners that have the air conditioning operating
during the morning and evening commutes. The last two entries on card
type 3P are the average acceleration and average deceleration on CBD
pathways.
For the short-term mode of operation, the input data on card type 3P
are similar to those for the annual mode, but the data are for only one
commute. The percentages of cold-starting and hot-starting vehicles on
pathways refer to the commute time of interest. These percentages should
apply to the area in which most of the commute vehicles begin their trips.
(The percentages may be omitted if pathway emissions are to be read di-
rectly (LPATH=2)). The temperature entered on card type 3P should be an
average temperature for the commute being analyzed. Percentage of vehicles
equipped with air conditioning that have their units in operation should
also apply to the single commute being modeled. Finally, average accelera-
tion and deceleration on CBD pathways is entered.
60
-------
Card Type 4PThe data to be input on card type 4P also depend on
the chosen operating mode for the model. If non-pathway (gridded) emis-
sions are to be input directly (LNPATH=2), omit card type 4P.
For both modes of model operation, the percentages of cold-starting
and hot-starting vehicles on the primary street network (nonpathways) are
input by locale type. For the annual mode, the percentages refer to the
morning commute; for the short-term mode, the percentages refer to the
commute of interest.
The five locale types are:
(1) Central business district
(2) Commercial/suburban
(3) Residential
(4) Industrial
(5) Rural and miscellaneous.
Card Type 5PThis card type is input only for the annual mode. If
nonpathway (gridded) emissions are to be input directly (LNPATH=2), it
should be omitted. Card type 5P contains the same information as card
type 4P (see previous paragraphs), but the percentages pertain to the
evening commute.
Card Type 6PThis card thpe is included only when a vehicle inspec-
tion/maintenance (I/M) program is in effect in the region to be modeled
(IMFLAG=1). The first input is the year the I/M program is to be imple-
mented. The next two entries are the stringency level of the program
and a flag indicating whether there is to be mechanic training. The last
two entries on the card are the earliest and latest model years included
in the I/M program.
61
-------
5.1.3 Commuter Exposure Model (CEMAP) Input Data
Card Type 1MThe data to be entered on card type 1M are general
in nature. First is an alphanumeric heading, which may be up to 20 char-
acters long. The heading may say anything che user would like to have
printed at the beginning of model output. Next, the date is input,
followed by four flags, specifying the desired mode of operation for the
model (annual or short-term, MODRUN), whether pathway emissions are to be
entered directly or calculated by the model (LPATH), and whether nonpathway
emissions are to be entered directly or calculated by the model (LNPATH).
The last entry is a flag to indicate whether the input data should be
printed along with the model ouput (NPRINT).
Card Type 2MThe first input on card type 2M is the number of path-
ways to be modeled: This parameter may be no larger than 5. Next, the
number of segments that define each pathway is entered (no larger than
20), followed by the total number of grid squares through which pathways
pass (up to 500). Finally, a conversion factor is input to convert to
miles any consistent units that were used to define the segment endpoint
coordinates.
Card Type 3MCard type 3M has a slightly different format and in-
put data for the two modes of model operation. Table 3 lists the data
required for each mode.
One card type 3M must be input for each segment of each pathway.
The program is currently dimensioned to accept up to five pathways.
(See Volume II for instruction on how to change these dimensions.) Note
that pathways must be numbered sequentially. See Section 5.2.1.1.3 re-
garding the required pathway and segment numbering convention. Cards
62
-------
of type 3M must be ordered such that data for all segments of the first
pathway are given, in numerical order by segment number, before informa-
tion on the next pathway is given. Then all segments of the second path-
way are input in numerical order, and so forth. Neither pathway nor seg-
ment number may be skipped.
For the annual mode, the first data listed on card type 3M are the
pathway and segment numbers and two flags defining segment type. Next,
if hourly volumes are known, the demand volumes in the inbound and out-
bound directions for an average hour of both the morning and evening
commutes are entered and the demand volume flags which follow are set to
the appropriate values. If volume data are in the form of ADT or AADT,
a single volume may be input but it must be input four times. The model
adjusts7'these volumes by time of day and direction of travel. The flags
are set to the appropriate values. If pathway emissions are to be input
directly, four emission rates, for the inbound and outbound directions
of the morning and evening commutes, should be input in lieu of the volumes.
If emissions are input, then speeds must also be input. Other inputs on
card type 3M include number of lanes for each direction, lane capacity,
lane width, and the endpoint coordinates of the segment. Note that seg-
ment length must be greater than or equal to segment width plus 6 m.
Capacity may be entered directly, if known, or a default input of 0 will
cause values internal to the model to be used. Similarly, speeds (morn-
ing and evening commutes, inbound and outbound) may be entered if known,
but a zero value will cause speed to be calculated from the capacity re-
straint relationships. If pathway emissions are input directly (LPATH=2)
with zero speeds, the model assigns speeds according to the type of link.
63
-------
The endpoint coordinates of the segment may be in any convenient, con-
sistent units and then converted to miles by the conversion factor speci-
fied by the user on card type 2M. However, the endpoint numbering con-
vention described in Section 5.2-1.1.3 must be followed.
At the end of card type 3M are two flags concerning data on inter-
sections. The first describes the existence and type of intersection
(including interrupted flow on expressways); the second is a flag that
denotes whether or not the user is supplying the intersection data; this
flag must be entered only for segments for which the previous flag was
given a value of 2.
Several of the parameters listed in Table 3 need not be input if
pathway emissions are read directly; these parameters are indicated as
such in the table. For the short-term mode, all of the data are entered
as described above with the exception of the demand volume/emission rate
and speed entries and a direction of commute flag. For the short-term
mode, if the inbound and outbound hourly volumes for the specified hour
of the commute being modeled are known, they are input in columns 10-15
and 16-21 and the appropriate flags are set. If the volume data are ADT
or AADT, follow the instructions given above for the annual mode. (If the
volumes are AADT they are further adjusted for season.) If pathway emis-
sions are to be input directly, the inbound and outbound direction emis-
sion rates for the specified hour of the commute being modeled should be
input in lieu of the volumes. Speeds for the inbound and outbound direc-
tions are input in columns 47-48 and 49-50. These may be zero, to invoke
capacity restraint speed computation, unless LPATH=2, in which case speeds
must be entered, otherwise the model will assign a speed. For the short-
64
-------
term mode, one additional item of data is required on card type 3M. A
flag (IBOUND) must be included that indicates whether the commute being
modeled is in the morning or evening.
Card Type 4MCard type 4M includes data that characterize the in-
terrupted flow conditions simulated by the commuter exposure model:
signalized intersections, toll booths, and freeway backups.
One card type 4M must be input for each segment having INTYPE dif-
ferent from zero on card type 3M. The input data requirements very de-
pending on the type of interrupted flow, and the data are grouped for
signalized intersections, freeway backups, and toll booths. Only the
data for the particular type of interrupted flow being simulated need be
entered for each segment. Special attention should be paid to the fact
that volume and capacity data entered on card type 4M must be per lane.
Following the pathway and segment identification, the first data
grouping is for signalized intersections. The demand volume and capacity
for the controlling, opposing intersection approaches are entered in the
following order: segment AM, opposing critical approach AM, segment PM,
opposing critical approach PM. Note that capacities must be greater than
or equal to one or division by zero may result. Also note that these
data are included whether the signal data are included or not. When the
model is used in the short-term mode, the data should be entered in the
position (inbound or outbound) appropriate to the direction of travel
of the commute being modeled. For example, if the commute being modeled
is an inbound morning commute, the capacity of the segment approach
should be entered in columns 24-27, the capacity of the cross-street
approach should be entered in columns 28-31, and columns 32-39 may be
left blank.
65
-------
Signal data on cycle length and green phase length are entered
next, if known. The model will calculate appropriate values from the
volume and capacity data if INTDAT is not equal to one on card type 3M.
Only the length of the green phase for the segment is needed.
The next data grouping is for freeway backups. The algorithms used
are similar to those for signalized intersections and so are the data.
The demand volume and roadway capacity are entered, on a per lane basis,
and the time duration of the backup is also entered. Again, in the short-
term mode only the relevant inbound or outbound data need to be entered.
he final data grouping is for toll booths. The number of toll booths
for AM (inbound) and PM (outbound) periods are entered for annual mode
operation. Only one need be entered for the short-term mode.
Card Type 5MAs with several other card types, the data included
on each type 5M differ for the annual and short-term modes.
For the annual mode, the number of commuters traveling the full
length of each pathway during the morning commute are input; although the
model is currently dimensioned for 5 pathways, the number could be in-
creased. In that case, the number of commuters traveling the full path-
way would be input 15 pathways to a card. This could take more than one
card, depending on the number of pathways. Beginning with a new card,
the same information should be input for the evening commute. This will
take another card (or more if the program has been redimensioned for more
pathways) which should follow the cards for the morning commute. Number of
commuters is found by multiplying the number of commute vehicles on the
pathway by the average vehicle occupancy.
For the short-term mode, the number of commuters traveling on the
pathway are input only for the commute of interest, (up to 15 pathways to
a card). 66
-------
Card Type 6MCard type 6M has a different format and input data
for the two modes of model operation. Table 3 lists the data required
for each mode.
For the annual mode of operation, the first input parameter is the
percentage of the average daily VMT on primary and secondary streets
that occurs during the average hour of the morning commute, followed by
the percentage that occurs during the average hour of the evening commute.
Lastly, the ratio of the VMT on secondary streets to the VMT on primary
streets that is typical for each locale type is input. (These ratios
may be omitted if nenpathway emissions are to be read directly (LNPATH=2).)
For the short-term mode of operation, the percentage of the average
daily VMT on primary and secondary streets during the specified hour of
the commute of interest should be entered. Often, this will be the hour
with the heaviest travel (and hence highest exposures). If the user is
not analyzing a "worst-case" commute, but rather an averagebut specific
commute, this percentage should apply to the average hour of the specific
commute. The ratio of the VMT on secondary streets to the VMT on primary
streets during the commute being analyzed that is typical for each locale
type is input last. If nonpathway emissions are to be read directly
(LNPATH=2), these ratios may be omitted.
Card Type_ 7MData describing the grid squares is entered on card
type 7M: One card should be input for each grid square through which a
pathway passes; the maximum number of grid squares allowed is 500. The
first data item to appear on card type 7M is the number of the grid square,
followed by the coordinates of the southwest corner of the grid square
and the length of one side of the grid square. The next entry is different
67
-------
when the model computes nonpathway source emissions and when emissions
are input directly. If the model is to compute nonpathway source emis-
sions (LNPATH=1), the next input is the average daily VMT on primary
streets in the grid square, followed by the locale type associated with the
the grid square. If emissions are input directly (LNPATH=2), the above
two inputs are omitted, and the average daily emission rate for the square
should be entered. The grid squares must be numbered sequentially and
input in numerical order (although the numbering system may be deter-
mined by the user). The units of the grid square coordinates must be
consistent with the units of the pathway segment coordinates.
Card Type 8MThe meteorological data are entered into the model on
card type 8M. As with some of the other card types, different formats
and data will be used for the two different modes of model operation.
For the annual mode, the meteorological data are in the form of
joint frequency distributions of wind direction, wind speed, and atmo-
spheric stability. A distribution consists of the fractions of time that
each combination of wind direction, speed, and stability occurs. For the
purposes of the commuter exposure model, "time" in the previous sentence
is defined as the morning commute period (usually 6-9 am) or the evening
commute period (usually 4-7 pm) for the area being modeled; the hours
which comprise a commute period may vary with area. Two distributions
must be input to the model, one describing the morning commute period
and the other for the evening commute.
The meteorological parameters are categorized into 16 wind direction
classes, 6 wind speed classes, and 6 stability classes. The value inter-
vals for the classes are given in Table 4. Note that the model will
68
-------
w
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69
-------
combine stability classes 4 and 5, day and night neutral, into one class
called 4, and class 6, stable, will be called class 5. The morning dis-
tribution should be input first in the following order: For stability
class 1, 16 cards (one for each wind direction class), 6 values per card
(for each wind speed class); then stability class 2, 16 cards, and so
forth 96 cards for each frequency distribution. The evening distri-
bution, ordered as described above, should follow the morning distribu-
tion.
For the short-term mode, values for wind speed, wind direction, and
stability should be entered for the hour of the commute of interest.
Stability class is defined as shown in Table 4 except class 4 is neutral,
class 5 is stable, and there is no class 6.
Card Type 9MCard type 9M is a data card used to cause optional
output to be generated by the model; it is included only when running
in the annual mode. If only some of the options available are to be used,
zeroes should be entered for all remaining inputs on the card. If no
options are called for, the card may be blank, but it still must be in-
cluded.
The first set of input values on card type 9M consists of the numbers
of those pathways for which the user wants the percentage of commuters on
each of the pathways in each of several exposure classes to be printed.
The output can be obtained for up to five pathways. Pathway numbers must
be input in increasing numerical order.
The next set of input values contains the numbers of those pathways
for which the user wants the probability of the commuters on each of the
pathways experiencing exposure levels in each of several exposure classes
70
-------
to be printed. As many as five pathway numbers can be input. Pathway
numbers must be input in increasing numerical order.
The next values to be entered on card type 9M allow the user to
observe the variation of exposure on a specific pathway with meteorologi-
cal conditions. First, the number of the pathway of interest is entered.
Next, the number associated with a particular combination of wind direction
class, wind speed class, and atmospheric stability class is entered. The
model allows up to 10 sets of meteorological conditions to be input. The
number associated with each combination of meteorological conditions is
taken from Table 5. The definitions of the value intervals for the wind
direction, wind speed, and stability referred to in Table 5 appear in
Table 4. Remember, the model will combine day and night neutral stability,
so there is really no difference in choosing either of the two classes.
Table 4 has been left showing six stability classes so it will correspond
to the format of the frequency distributions.
The last parameter to be entered on card type 9M is a flag calling
for graphical output. Remember that the user must furnish the plotting
routine, either as a part of subroutine GRAF or as a separate routine,
called from GRAF. (See Section 5.3.1.)
5-2 Preparation of Input Data
5.2.1 Definition of the Modeling Approach
The crux of the commuter exposure modeling problem is defining the
modeling area. The modeling area should coincide with the Standard Metro-
politan Statistical Area (SMSA) or the Air Quality Control Region (AQCR)
bounding the urban core. Such a practical choice has been made because
those areas usually encompass the region that influences and is impacted
71
-------
TABU S. NUMBERS ASSOCIATED WITH COMBINATIONS OF METEOROLOGICAL PARAMETERS
Stability and
Wind Speed
Classes
Stability I
Wind Speed I
Wind Sp««d 2
Wind Sp««d 3
Wind Sp««d 4
Wind Sp««d S
Wind Sp««d 6
Seabillcy 2
Wind Speed 1
Wind Spt«d 2
Wind Sp««d 3
Wind Speed 4
Wind Sp««d 3
Wind Speed 6
Stability 3
Wind Speed 1
Wind Sp««d 2
Wind Speed 3
Wind Speed 4
Wind Speed 5
Wind Speed 6
Stability &
Wind Speed 1
Wind Speed 2
Wind Speed 3
Wind Speed 4
Wind Speed 5
Wind Speed 6
Stability S
Wind Speed 1
Wind Speed 2
Wind Speed 3
Wind Speed 4
Wind Speed 5
Wind Speed 6
Stability 6
Wind Speed 1
Wind Speed 2
Wind Speed 3
Wind Speed 4
Wind Speed S
Wind Speed &
1
1
2
3
4
5
6
97
98
99
100
101
102
193
194
195
196
197
198
289
290
291
292
293
294
38S
386
387
388
389
390
481
482
483
484
485
486
2
7
8
9
10
11
12
103
104
105
106
107
108
199
200
201
202
203
204
295
296
297
298
299
300
391
392
393
394
395
396
487
488
489
490
491
492
3
13
14
15
16
17
18
109
110
111
112
113
114
205
206
207
208
209
210
301
302
303
304
305
306
397
398
399
400
401
402
493
494
495
496
497
498
4
19
20
21
22
23
24
115
116
117
118
119
120
211
212
213
214
215
216
307
308
309
310
311
312
403
404
405
406
407
408
499
500
501
502
503
504
5
25
26
27
28
29
30
121
12:
123
124
125
126
217
218
219
220
221
222
313
314
315
316
317
318
409
410
411
412
413
414
SOS
506
507
508
509
510
6
31
32
33
34
35
36
127
.128
129
130
131
132
223
224
225
226
227
228
319
320
321
322
323
324
415
416
417
418
419
420
511
512
513
514
515
516
Wind
7
37
38
39
40
41
42
133
134
135
136
137
138
229
230
231
232
233
234
325
326
327
328
329
330
421
422
423
424
425
426
517
518
519
520
521
522
Direction
3
43
44
45
46
47
48
139
140
141
142
143
144
235
236
237
238
239
240
331
332
333
334
335
336
427
428
429
430
431
432
523
524
525
526
527
528
9
49
50
51
52
53
54
145
146
147
143
149
150
241
242
243
244
245
246
337
338
339
340
341
342
433
434
435
436
437
438
529
530
531
532
533
534
Class
10
35
56
57
58
59
60
151
152
153
154
155
156
247
248
249
250
251
252
343
344
345
346
347
348
439
440
441
442
443
444
535
536
537
538
539
540
11
61
62
63
64
65
66
157
153
159
160
161
162
253
254
255
256
257
258
349
350
351
352
353
354
445
446
447
443
449
450
541
542
543
544
545
546
12
67
68
69
70
71
72
163
164
165
166
167
168
259
260
261
262
263
264
355
356
357
358
359
360
451
452
453
454
455
456
547
548
549
550
551
552
13
73
74
75
76
77
78
169
170
171
172
173
174
265
266
267
263
269
270
361
362
363
364
365
366
457
458
459
460
461
462
553
554
555
556
557
558
14
79
80
31
82
33
34
175
176
177
178
179
180
271
272
273
274
275
276
367
368
369
370
371
372
463
464
465
466
467
463
559
560
561
562
563
564
15
as
86
87
88
89
90
181
182
183
134
135
186
277
278
279
230
231
282
373
374
375
376
377
378
469
470
471
472
473
474
565
566
567
568
569
570
16
91
92
93
94
95
96
187
188
189
190
191
192
233
284
285
286
287
288
379
380
381
382
383
384
475
476
477
478
479
480
571
572
573
374
575
576
72
-------
by the urban core, and because a wealth of requisite data is routinely
available for them. Generally, the SMSA .and AQCR boundaries are the same.
This section describes a methodology for defining the modeling area
in terms of the two important types of emission sources in a commuter
exposure model: (1) line sources, or commuter pathways; and (2) non-
pathway sources (the "background source effects") from the remaining
roadway network.
5.2.1.1 Commuter Pathways
The most critical.aspect in defining the modeling area is choosing
the appropriate commuter pathways. The commuter exposures that are cal-
culated and the statistics that are derived all depend directly on the
pathways that are defined.
Fortunately, the major commuting pathways, or "commuting corridors,"
are well-defined and recognizable if one is familiar with the modeling
area. Often, just the physical size of the roadway, or, more properly,
its capacity, will identify it as a commute route. The commuting path-
ways are also corridors of development, fostered largely by the access
to the urban core that the route provides.
From a methodological standpoint, however, it is suggested that the
user of the commuter exposure model seek advice in defining the major
commuting routes. The local transportation department, the metropolitan
transportation planning authority, or the coucil of governments should
be able to provide this assistance. The remainder of this discussion
provides the criteria that should be presented to the transportation
expert to help him help the model user in defining the commuting pathways.
73
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5.2.1.1.1 Origin-Destination Zones
A key part of the transportation planning process is the estimation
of trends of demand for transportation services. Estimates of demand
are based on a knowledge of existing demand, and on known and planned
land-use and demographic characteristics of the planning region. Tools
that help quantify this demand are the origin-destination survey and the
additional data that can be generated from the survey results.
Typically, the region is divided into subareas, or zones. They are
of variable size, depending on population density and other demographic
characteristics, and on the roadway network. The zones are smaller and
denser in the urban core than in outlying areas. For example, the trans-
portation planning region for Pittsburgh encompasses about 9000 square
miles and contains approximately 900 zones. However, about 100 of them
are in the downtown, "Golden Triangle," are of the city.
In simplest for, the origin-destination data will supply the daily
trips between zones. Thus, they may be broken down by trip purpose, by
trip purpose pairs indicating the direction of flow (home-work, work-home),
and by time of day. For the commuter exposure model, trips to and from
work are the main ones of concern in defining the commuting routes.
Fortunately, work trips have the most tractable statistics. They are
likely to vary the least from one weekday to the next and they occur dur-
ing fairly well-defined time periods. They are relatively insensitive
to weather and travel conditions. Additionally, even if the trip is
identified only as a work trip, it can be assumed to be from home to work
in the morning and from work to home in the evening. The origin-destination
data are usually presented as a matrix, with an accompanying map indica-
ting the zones. The matrix elements are the numbers of trips between
origins and destinations.
74
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The purpose of origin-destination data for commuter exposure modeling
is to define the commute pathways. Accepted accuracy for the data is
"t 20 percent, so they should not be used to generate traffic volumes.
Their utility lies in showing the spatial flow of home-work and work-home
trips. Once the spatial flow is known, the key roadways can be deter-
mined, and traffic volumes can be taken from volume flow maps.
Given fairly comprehensive origin-destination data, it would be
tempting to try to model all commuters. All of the work-related trips
are identified, and one could seemingly define routes for them and model
the exposures. But the futility of doing so becomes readily apparent
when the Pittsburgh example is considered: the total number of possible
origin-destination pairs is 900^, or 810,000. Even as few as 100 zones
would yield 10,000 combinations. While not every combination will produce
a work-related trip, the percentage is large enough to render such an
approach impractical.
5.2.1.1.2 Definition of Commute Pathways
For commuter exposure modeling to be a manageable problem, a rea-
sonable number of major commuting routes or pathways must be defined
even though a plethora of origin-destination data is available. The
number of pathways will be a function of the size of the modeling area;
the program is presently dimensioned for five pathways. (If more than
five pathways are defined, the user is referred to Volume II of the guide
for instructions on how to increase the dimensions of the program.) Those
routes will generally have the highest number of vehicle miles traveled
(VMT) by commuters. Again, it is recommended that the advice of a trans-
portation planner or traffic engineer familiar with the area be sought
when the routes are defined.
75
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It is recognized that the commute pathways defined in such a manner
will not carry all commuters. However, the method described here should
include a great majority of the commuters who are at risk of experiencing
high exposures on the order of the air quality standards. Most of the
extensive commuting will be done along the defined pathways, both in time
and distance, and the pathways will by definition carry high volumes of
traffic. Although their total number may be close to that on the path-
ways, the commuters "missed" will, on the average, be traveling shorter
times and distances on less heavily traveled roads. They are not con-
sidered to be at risk to high pollutant exposures during their commute.
5.2.1.1.3 Characteristics of Commute Pathway Segments
In general, commute pathways will include different types of road-
ways. Two types of roadways that are prime candidates to be commute
pathways are expressways and arterials. A pathway Tnay be composed of
both. The Highway Capacity Manual (HCM) defines an expressway as "a
divided arterial highway for through traffic with full or partial control
of access and generally with grade separations at major intersections."
(A freeway is defined as an expressway "with full control of access.")
The term arterial is intended to have the meaning that the HCM applies
to a major street or highway: "an arterial highway with intersections
at grade and direct access to abutting property, and on which geometric
design and traffic control measures are used to expedite the safe move-
ment of through traffic." In addition, commuting trips usually begin
and end on local surface streets, and these too will be part of the
pathway.
76
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To describe a commute pathway for the purposes of the commuter ex-
posure model, the pathway should be numbered sequentially and then divided
into segments or links. No pathway numbers may be skipped. The follow-
ing are criteria for defining different pathway segments:
Change of the roadway type, e.g., freeway to arterial or
arterial to surface street.
Abrupt change in roadway direction or traffic flow direction.
Presence of a major intersection.
Abrupt change in vehicle volume.
Beginning and ending parts of pathway.
For a given commute pathway, several segments will be defined, and
each segment will be described with the input data listed in Section
5.1.3 for card type 3M. In addition, card type 4M must be included for
all segments on which there are intersections, freeway backups, or toll
booths.
A note should be made regarding the definition of the beginning and
ending segments of the pathway. In general, commuting trips begin on
local surface streets and "collectors." Often, they will not even begin
in the same zone that they are in when they enter the pathway. To handle
the exposure during the approach to and departure from the pathway, minor
pathways should be used that are representative of the travel to and from
the route. If such a pathway cannot be found, one should be "invented,"
with a location, volume, and characteristics typical of the kind of route
on which commuters in the region of the pathway endpoint would travel.
Here again, the local transportation authority can give guidance on the
definition of such a segment.
77
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For both the annual and short-term modes of model operation, the
commute pathway segments should be defined in the following way. A
segment is a section of roadway on which there is travel in both direc-
tions. The first segment of the pathway is the beginning of the pathway
for the morning commute. The segments must be numbered sequentially,
from one to no more than 20, for each pathway. The model distinguishes
one segment from another by both pathway and segment number, so for each
pathway, segment numbering should begin with one. No segment numbers
may be skipped.
Note that the convention specified above regarding segment number-
ing applies to the short-term mode of operation even if the commute being
analyzed is the evening commute; that is, the segment numbered as one is
the first inbound segment of the morning commute.
There is also a convention that must be adhered to regarding the
numbering of the segment endpoints. The first end of the segment, whose
coordinates are (XI, Yl), is the beginning end of the segment in the
direction of the morning commute, and (X2, Y2) is the other end of the
segment. Again, this convention applies to both modes of model operation,
even if the commute being analyzed in the short-term mode is the evening
commute.
5.2.1.2 Non-Pathway Sources
5.2.1.2.1 Definition of Grid System
Non commute pathway pollutant emissions are treated on a grid square
basis. The user should overlay the study area with a grid system, and
in general the grid squares should have dimensions in the neighborhood
of 2 km by 2 km. For convenience, the model allows input of grid squares
73
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of different sizes. However, if this option is used, care must be
taken that grid squares do not overlap and that all points on each seg-
ment fall with a grid square.
The coordinate system used to define the grid must be consistent
with the system used to define commuter pathway coordinates. Grid squares
are defined by their lower left corner coordinates.
The grid square coordinates, and the other grid square descriptors
described in Section 5.1.3, need be input to the model for only those
squares through which commuter pathways pass. Figure 5 illustrates a
typical commuter pathway network, overlaid by a uniform grid system.
The shaded grid squares are those for which input data are required.
They contribute appreciably more to the concentrations experienced on
the pathway than do other squares.
5.2.1.2.2 Apportionment of Vehicle Miles Traveled on the
Primary Network
The primary street network is defined as those roadways carrying
a major amount ofthe total vehicle miles traveled (VMT) on the entire
roadway network of a metropolitan area. In general, the primary network
can be considered to be those streets for which average daily traffic (ADT)
values are available. For the commuter exposure model, the primary net-
work should not include roadways that are commuter pathways.
Because the major part of the concentration on a commuter pathway
is expected to result from vehicles traveling on the pathway itself,
the part of the concentration resulting from the other non-pathway road-
ways in the vicinity can be approximated. The approximation used in the
79
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FIGURE 5 SAMPLE COMMUTER PATHWAY NETWORK WITH GRID OVERLAY
Data must be input for shaded squares.
30
-------
model consists of an aggregation of primary network emissions into grid
squares, with the assumption of a uniform emission rate across the square.
This approach saves the user considerable effort that would be required
if each raodway in the primary network, and its associated characteris-
tics, were used as model input.
Therefore, once the grid system is defined, the user must apportion
the total VMT of primary network streets among the grid squares and input
that information for the appropriate squares. The recommended approach
for the apportionment is the following. A volume flow map showing the
ADT on the major roadways in the study area should be overlaid with the
grid system. The length of the portion of each roadway that lies in the
grid square should be multiplied by the number of vehicles traveling on
that roadway each day, yielding the VMT on the portion of the roadway
that lies in the grid square. This procedure should be followed for
each portion of roadway in the grid square; the total VMT in the grid
square will be the sum of the individual roadway VMTs. The measurement
of roadway lengths and volumes need not be exact, because the primary
network emissions and dispersion treatments involve a number of approxi-
mations.
The VMT for each grid square through which a commuter pathway passes
must be input to the model; it is used to generate an emission rate for
the square. Care should be taken so that the VMT on commuter pathways
is not included in the VMT of a grid square.
In general, the results of the foregoing procedure is used to com-
pute emissions from the primary network. However, the model allows the
user to input an average daily emission rate for each grid square, in lieu
31
-------
of VMT. This option would be used when gridded emissions for the study
area are readily available from another source or when the user has ac-
cess to the projections generated by the Federal Highway Administration's
(FHWA) battery of computer programs. In the latter case, the user may
opt to run the APRAC-3 emissions module, which takes the FHWA historical
traffic files as input (deleting, of course, the commuter pathways), to
produce a gridded emission inventory. The values from this inventory
may be used as input to the commuter exposure model. For further details
on the APRAC-3 emissions module, see Simmon, et al. (1981). If emission
rates are input directly, note that they should be daily rates, as the
model will automatically reduce the daily rates to hourly rates for the
morning and evening or short-term commutes.
5.2.1.2.3 Apportionment, of Secondary Newwork VMT
The secondary traffic network consists of all streets not in the
primary network or commuter pathways. The approach used for assigning
secondary traffic to grid squares assumes that the ratio of secondary
traffic to primary traffic varies by locale (or by land use). For in-
stance, virtually all of the streets in the Central Business District
(CBD) are likely to be important enough to be included in the primary
traffic network. Similarly, very little traffic in rural areas is likely
to be found off the major roadways. In residential and industrial areas,
a large fraction of the total traffic may use secondary streets. The
user estimates the ratio of secondary to primary traffic for each locale
type. The locale type of each grid square or group of grid squares for
which data are required also will be input. The model will allot secondary
network VMT on the grid squares in proportion to the product of the VMT
82
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on the primary network in that square and the ratio of the secondary to
primary traffic associated with the locale type of the square. A list
of locale types appears in Table 6.
Table 6
LOCALE TYPES
1. Central business district
2. Commercial areas in the core of the city,
or suburban centers
3. Residential areas
4. Industrial areas
5. Rural and miscellaneous
If gridded emissions are input directly, as discussed in the previous
section, secondary network emissions should be included in that inventory.
5.2.2 Data Sources
5.2.2.1 Emission Factor Preprocessor Data
Card Type IPAll of the input data on card type IP are determined
by the user, depending on the place, time, and type of application being
considered.
Card Type 2PCard type 2P contains the distributions of LDV regis-
trations by model year and the total VMT by vehicle type. These data are
sometimes available from the state Department of Motor Vehicles, the state
or county Department of Transportation, or the local Transit District or
planning agency. Private firms offer data of this type, for a number of
cities, for sale. If no other information is available, the national
33
-------
average distributions are stored in the model and are available as
default parameters.
Card Type 3PData describing the percentages of cold- and hot-
starting vehicles and vehicle air-conditioner usage are in genera],
difficult to find. The transportation agencies listed for type 2P data
are places the user is advised to contact. Guidance for determining such
percentages is given in the EPA publication (Midurski et al., 1977) and
the user is also referred to this document. The temperatures required
on card type 3P are available from the National Weather Service or the
National Climatic Center.
Card Types 4P and 5PSources of percentages of cold- and hot-starting
vehicles are discussed under card type 3P..
Card Type 6PThis card type contains data describing an inspection/
maintenance program. Such data should be available from the state or local
transportation planning agency.
5.2.2.2 Commuter Exposure Model Data
Card Type 1MThe data contained on card type 1M are determined by
the user, according to the application intended.
Card Type 2MThis card type describes the number of pathways, seg-
ments, and grid squares in the modeling area and includes a map unit
conversion factor. These data will be determined by the user after de-
fining the commute pathways and grid system.
Card Type 3MThis card describes the basic traffic and roadway data.
The data on this card should generally be available from the local trans-
portation planning agency. Volume or emission rate must always be input,
as must the flags describing the data format, the type of roadway, and
84
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the existence of a backup or an intersection. If emission rates are input
directly, then speed must also be input so that exposure time may be cal-
culated; if volumes are input rather than emission rates, speed may be
input or the model will calculate it if it is omitted. Note that card
type 3M calls for four volumes, or emission rates and speeds, per segment
in the annual mode and only two when the model is exercised in the short-
term mode.
Card Type 4MThis card type continues the traffic inputs for free-
way backups, toll booths, and modal flow calculations. The traffic signal
data may be input directly or calculated by the model. If they are cal-
culated by the model, then the volumes and capacities for the opposing
approaches must be input. The data for toll booths and freeway backups
must always be included when these cases arise.
Card Type 5MThe data on this card, the number of commuters on each
pathway, should be available from the local planning agency or council of
governments. Alternatively, it may be estimated from traffic volumes and
vehicle occupancy rates.
Card Type 6MAll. of the information contained on card type 6M should
be available from the state or county Department of Transportation or the
local Transit District or planning agency. The percentages of average
daily VMT on primary and secondary streets during an average hour of the
morning and/or evening commutes may have to be derived by the user from
the hourly volume measurements that are available for various roadways.
An average diurnal distribution may be available, from which the percent-
ages could be easily computed. The secondary street network to primary
street network VMT ratios may have to be determined by an "educated guess"
of the local traffic personnel.
85
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Card Type 7_MThis card type contains grid square information. The
user determines the coordinates of the grid squares and their daily VMT
on primary streets using the method described in detail in Section
5.2.1.2.2. The volumes on the primary street network can be obtained
from the state, county, or local transportation and planning agencies.
The locale type of the grid squares can be derived from land-use maps
available from the local planning agency. Gridded emission rates are
sometimes available for metropolitan areas; see the discussion of this
in Section 5.2.1.2.2.
Card Type 8MCard type 8M contains meteorological data. The source
of such data is generally the National Climatic Center (NCC). Historical
records of hourly surface observations for many cities are available from
the NCC. In addition, JICC has a computer program, called STAR, which pro-
cesses hourly observations to produce climatological joint frequency dis-
tributions of the type required by the model in the annual mode of opera-
tion. The user should inquire as to whether NCC could generate such dis-
tributions for the morning and evening commute period. The distributions
could be generated by the user from hourly surface observations.
Card Type 9MThis card contains a number of flags calling for various
optional model outputs. The user sets these flags according to what out-
put is to be generated.
36
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5.2.3 Additional Recommendations
This section contains recommendations for generating various data
necessary to the model that may be difficult to obtain. In some instances
the information given here will help clarify how a model input parameter
is determined using other available information. The following discussions
center on traffic parameters.
Additional notes regarding choice of the terminating (or originating)
pathway segment may be helpful to the model user. When the commute paths
enter the CBD, and the queueing calculations are applied, single routes
must be chosen for each commute path, terminating at a reasonable loca-
tion. This can be a central point in the CBD, or there can be a separate
terminus for each path, such as locations of relatively high employment
density within the urban core. For paths not going to the CBD, the end
points should be clear because they had to have been identified already
in order to support a commute path not involving the CBD.
Demand volume may be obtained from volume flow maps or tabulated
volume data available from the local transportation agency. Ideally,
hourly data should be available, but often only the average daily traffic
(ADT) or annual average daily traffic (AADT) can be obtained. In the
latter "cases, factors must be applied to arrive at estimated hourly
volumes during commuting periods. Those factors should be obtained from
the local transportation agency so that they are representative of the
area. If they are unavailable, national average values should be used.
Default values are contained in the model in BLOCK DATA. As an example,
Table 7 shows the percentage of weekday AADT by hour, land use type, and
direction of travel on freeways in the San Francisco Bay Area. Table 8
supplies the same data for nonfreeway streets.
87
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Table 7
EXAMPLES OF WEEKDAY DIURNAL TRAFFIC CYCLES ON
FREEWAYS IN THE SAN FRANCISCO BAY AREA
Hour
Ending CBD
(local
time) NBf
00
01
02
03
04
05
06
07
08
09
010
on
012
013
014
015
016
017
018
019
020
021
022
023
Total
.017
.010
.006
.004
.004
.007
.022
.057
.055
.044
.044
.050
.057
.053
.059
.078
.110
.099
.060
.045
.033
.035
.028
.024
1.001
*
SB
.014
.007
.004
.004
.006
.016
.062
.097
.075
.052
.051
.054
.049
.056
.063
.067
.072
.064
.047
.039
.027
.027
.026
.022
1.001
Courier cial
NB SB
.013
.004
.004
.002
.003
.005
.033
.127
.130
.066
.048
.043
.047
.042
.045
.051
.070
.073
.055
.042
.031
.026
.020
.017
.998
.012
.006
.004
.001
.001
.005
.026
.079
.086
.049
.039
.040
.043
.046
.052
.071
.091
.103
.074
.048
.035
.033
.029
.025
.998
Residential
NB SB
.007
.003
.002
.001
.000
.009
.077
.175
.131
.056
.045
.041
.039
.042
.041
.057
.059
.059
.045
.048
.032
.021
.023
.013
1.001
.017
.005
.003
.001
.001
.001
.009
.041
.049
.034
.032
.034
.035
.035
.043
.070
.163
.181
.101
.051
.028
.024
.025
.017
1.000
Industrial
NB SB
.009
.006
.004
.003
.005
.011
.055
.094
.072
.059
.032
.058
.054
.058
.059
.070
.083
.068
.051
.039
.033
.034
.023
.018
.996
.012
.006
.004
.003
.004
.008
.047
.069
.053
.052
.055
.056
.052
.061
.063
.073
.088
.097
.067
.041
.029
.026
.018
.019
1.M3
Rural
EB WB
.022
.011
.007
.005
.005
.007
.019
.053
.045
.045
.045
.047
.043
.050
.053
.070
.099
.101
.095
.045
.036
.039
.033
.025
1.000
.011
.006
.005
.004
.007
.023
.110
.117
.083
.061
.054
.049
.045
.046
.050
.050
.065
.058
.042
.037
.021
.019
.021
.017
1.001
These data are from Seattle; others are San Francisco Bay Area Locales.
Directions:
NB Northbound
SB Southbound
EB Eastbound
WB " Westbound
83
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Table 8
EXAMPLES OF WEEKDAY DIURNAL TRAFFIC CYCLES OH
NONFREEWAY STREETS IN TH£ SAI1 FRAIJCISCO BAY AKEA
Hour
Ending CBD*
(local
timet NB SB
00
01
02
03
06
,05
06
07
08
09
010
Oil
012
013
014
015
016
017
018
019
020
021
022
023
Total
.013
.006
.003
.004
.002
.003
.017
.035
.036
.039
.048
.067
.104
.068
.070
.071
.095
.081
.057
.048
.046
.039
.028
.020
1.000
.015
.008
.006
.004
.004
.007
.027
.061
.052
.040
.050
.059
.078
.081
.070
.066
.077
.072
.049
.043
.047
.036
.026
.021
0.999
Comnercial
EB WB
.008
.004
.003
.002
.002
.007
.036
.101
.081
.051
.052
.057
.064
.066
.061
.065
.068
.052
.052
.052
.045
.028
.025
.018
1.000
.014
.007
.005
.003
.002
.002
.010
.031
.041
.038
.046
.055
.068
.060
.064
.073
.097
.121
.081
.058
.035
.038
.031
.019
0.999
Residential
NB SB
.006
.003
.002
.002
.003
.015
.078
.149
.077
.059
.052
.051
.052
.049
.058
.071
.056
.052
.055
.043
.021
.017
.017
.011
0.999
.021
.010
.005
.003
.002
.003
.009
.033
.036
.034
.040
.053
.057
.064
.067
.074
.099
.079
.052
.036
.032
.039
.027
.019
1.002
Industrial
EB WB
.015
.007
.005
.004
.002
.004
.024
.086
.057
.049
.051
.053
.057
.064
.067
.074
.099
.079
.052
.036
.032
.039
.027
.019
1.002
.010
.004
.004
.002
.005
.018
.066
.103
.079
.001
.057
.057
.056
.056
.061
.062
.079
.065
.054
.031
.019
.018
.016
.016
0.999
Rural
EB WB
.007
.002
.002
.001
.012
.012
.056
.161
.091
.058
.058
.055
.045
.052
.062
.066
.064
.059
.054
.032
.017
.012
.019
.015
1.001
.016
.009
.005
.002
.004
.003
.017
.054
.046
.037
.051
.052
.048
.051
.057
.073
.127
.125
.063
.035
.030
.040
.031
.022
0.998
These data are from Seattle, others are San Franciosco Bay Area Locales.
Directions:
NB
SB
EB
VB
Northbound
Southbound
Eastbound
Westbound
89
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The capacity service volume in vehicles per hour of green is deter-
mined using the nomograph, Figure 6. The user must know the percentage
of trucks and buses, left turners, right turners, the location within
the metropolitan area, the size of the metropolitan area, and whether
or not the intersection is located in the CBD. The nomograph provides
a solution for a two-way urban street with parking. The solution for
that type of intersection is the most conservative estimate of capacity
in the Highway Capacity Manual. If a street has no parking within 250
feet of the intersection, eight feet can be added to the curb-to-center
line width (Wa) and a conservative solution will still result from use
of the nomograph.
Average route speed is another input parameter. The local trans-
portation agency should be able to offer advice on average route speed
estimates for interrupted flow portions of pathways. As a default,
the CURVE I in Figure 7 has been fitted to an equation and included in
the model. . The curves rely on volume-to-capacity ratios as input, and
they estimate an average speed for coordinated and uncoordinated traffic
signals. As a comparison with Figure 7, which is based on national data,
Figure 8 shows site-specific curves specifically for the Washington, D.C.
metropolitan area (see Metropolitan Washington Council of Government).
If a transportation agency cannot supply average speed data, they may
be able to provide curves similar to those in Figure 8, but based on
data from the local region.
Usually the signal cycle time is the sum of the green phase times
and amber times for all phases. When overlapping phases occur, the cycle
time is the sum of the left turn and through phases plus amber time,
90
-------
MIU t *» M«na»our*M »zi
AC nout MCTO* «aiun*KNr
MtnotourAN
tui Kir. (loopii
Ow tew
1008
7M
JOO
m
at
175
n
0.70
1.00
O.»f
Q.94
O.»l
O.*f
O.M
O.U
0.7T
a.Ji
1 S3
1.11
0 94
0.*4
«.«
O.tl
O.U
o.a
M*« HOU* '
O.M ' O.U
I.IOJ .M
1.07 .11
1 04 3t
1.01 .0*
O.H | .01
o.n ; .00
O.fl O.f7
0.17 1 O.tl
«c rot
o.n
.,»
.1*
13
tl
.01
.01
.07
O.H
o.»i i i.oa
Ml .?»
.Jl .O
. a ! .IT
.MJ .1^
.97 | ..1
.o> i 'o»
o
^
It,
o
5
f
o
z*
o
0
B
O
O
Ul
o
Ul
u
a:
hJ
in
a.
u
1-4800 |
4400 |
£
4000 1
«
(-3600 -
3200 <
u
O
L-2800 o
at
u
2400 ^
L-2000 ^
u
1600 >
u
(A
1200 >
h BOO
o
^^ Add 8 feet to the approach width if there is no parking.
When the peak hour factor is known, use table above to determine MP;
when peak hour factor is not known use population directly.
FIGURE 6 SERVICE VOLUME OF A SIGNALIZED INTERSECTION APPROACH
91
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URBAN AND SUBURBAN ARTERIALS
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
FIGURE 7 TYPICAL RELATIONSHIPS BETWEEN V/C RATIO AND AVERAGE
OVERALL TRAVEL SPEED, IN ONE DIRECTION OF TRAVEL,
ON URBAN AND SUBURBAN ARTERIAL STREETS
FIGURE 8 OPERATING SPEED RELATED TO LEVEL OF SERVICE,
ROUTE TYPE VOLUME/CAPACITY RATIO AND RING
92
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when there is no simultaneous green indicator. The capacity of an ap-
proach is the sum of the capacities for each through or turning move-
ment on the approach.
5.3 Model Output
5.3.1. Description of Output
The commuter exposure model output differs for the two modes of
model operation. The commuter exposure statistics calculated and printed
in each mode of operation are discussed in the following sections. A
summary of the output for each mode is given in Table 9.
5.3.1.1 Short-term StatisticsWhen the model is run
in the short-term mode, commuter exposures are output in several forms.
One is a list of the pathway, non-pathway, and total exposures, travel
time, and average concentration on each pathway, as well as the area
source concentration on the last segment of each pathway for the input
worst-case meteorological and traffic conditions. Next is the average
exposure for all pathways and the standard deviation of exposure over
all pathways. Finally, data are generated that can be used to construct
two histograms. To compute the histogram data, the model divides the
range of exposures found on all pathways into several classes. For each
class two paramters are listed: the percentage of the commuting popula-
tion treated by the model that experience exposure-levels in the class;
and the probability of experiencing the exposure levels in the class (i.e.,
the percentage of time commuters are exposed to the levels of the expo-
sure class).
The histogram data are produced by: assigning a range of exposure
values to be included in each exposure class; determining which class the
93
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TABLE 9. COMMUTER EXPOSURE MODEL OUTPUT
Short-Tenn Mode
Annual Mode
List of pathway, non-pathway, and
total exposures, travel time, and
average concentration on each
pathway and area source concen-
tration on last segment of each
pathway
Average and standard deviation of
exposures on pathways in modeled
region
Percentage of commuters in each
of several exposure classes
Probability of experiencing ex-
posure levels in each of several
exposure classes
Annual i^erage pathway exposure
Annual average morning and evening
pathway, non-pathway, and total
exposures, travel times, and con-
centrations on each pathway
Percentage of commuters in each of
several exposure classes (for all
pathways)
Probability of experiencing expo-
sure levels in each of several
exposure classes (for all pathways)
OPTIONPercentage of commuters in
each of several exposure classes
(on up to 5 single pathways)
OPTIONProbability of experiencing
exposure levels in each of several
exposure classes (on up to 5 single
pathways)
OPTIONPathway exposure associated
with up to 10 different sets of
meteorological conditions
94
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total exposure value on each pathway lies and assigning the pathway's
commuters and travel time multiplied by commuters (commute-seconds) to
that class; and then tabulating the number of commuters and the commuter-
weighted travel times in each class. Dividing the number of commuters
associated with each class by the total number of commuters on the path-
way network yields the percentage of commuters exposed to each range of
exposure values. Dividing the commute-seconds associated with each class
by the total commute-seconds on the pathway network gives the probability
of occurrence of the exposure levels in each class. These calculations
are made in subroutine SHORTY, which also prints the output.
To further clarify how the histogram data are computed, consider the
following simple example. Assume two pathways are modeled. The first
pathway has an exposure of 8.5 gsec/m , a 350 second travel time, and
carries 8000 commuters. The second pathway has an exposure of 7 gsec/m ,
a 250 second travel time and carries 9000 commuters. The exposure class
intervals are from 0-0.9 gsec/m , 0.9-1.8, and so forth, with the last
interval being 8.1-9.0. For the first pathway, the model will determine
that its exposure lies in interval 10, and it will store its 8000 commuters
in a bin for that interval and its 2.8 x 10 commute-seconds (8000 commuers
times 350 seconds) in another bin for that interval. For the second path-
way, the model will determine that its exposure lies in interval 8 (6.3-7.2),
and it will store its 9000 commuters in the interval 8 "commuters" bin and
its 2.25 x 106 commute-seconds (9000 x 250) in the interval 8 "commute-
second" bin. The model then adds the numbers in the 10 "commuters" bins
to find the total cummters (in the example this is 8000+9000=17,000) and
adds the numbers in the 10 "commute-second" bins to find the total commute
95
-------
seconds (2.8 x 106 + 2.25 x 106 - 5.05 x 106). Finally, the total number
of commuters in each bin is divided by the total number of commuters in
all bins and the results are multiplied by 100 to give the percentage
of commuters exposed to the range of exposure values represented by each
interval. In the example 47 percent (100 times 8000/17000) of commuters
modeled experience exposures between 8.1 and 9.0 gsec/m^, and 53 percent
(100 times 9000/1700) experience exposures between 6.3 and 7.2 gsec/m .
A similar procedure is followed for commute-seconds. In the example
there is a 55 percent probability (199 times 2.8 x 106/5.05 x 106)
of experiencing exposures between 8.1 and 9.0 gsec/m^ and a -45 percent
probability (100 times 2.25 x 10^/5.05 x 10^) of experiencing exposures
between 6.3 and 7.2 gsec/m .
96
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5.3.1.2 Annual StatisticsSeveral forma of annual statistics
are produced by the model when it is in the annual mode. (Subsets of
those statistics nay b« output at the user's discretion.) The model first
prints the annual average morning and evening pathway, non-pathway, and
total exposures, travel times, and concentrations on each pathway. Next,
the model prints the annual average exposure over the modeled region. Two
kinds of annual histogram data are then generated: the percentage of
commuters exposed to various classes (ranges) of exposure; and the prob-
ability of experiencing various classes of exposure (i.e., the percentage
of time commuters are exposed to those classes).
The model produces both of these types of statistics by the
process described below. The model begins with the first pathway. As
stated previously, several semi-normalized exposures are computed for
each pathway: 480 normalized exposures arising from non-street-canyon
pathway sources (for 16 wind directions, five stabilities, 3 wind speeds,
and morning and evening emission rates); two normalized exposures result-
ing from street-canyon sources (for morning and evening emission rates);
and ten normalized exposures resulting from nonpathway sources(for five
stabilities, and morning and evening emission rates). The model takes
the first of the 430 cases (one combination of wind direction, stability,
wind speed, and morning or evening emissions) and divides the exposure
by one of six wind speed values. Next, the normalized street-canyon
exposure for the corresponding time (morning and evening) is divided by
the same wind speed plus 0.5. Finally, the normalized non-pathway source
exposure for the corresponding time and stability is divided by the
wind speed, and all three exposure values are summed. The model deter-
97
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mines the appropriate exposure class for this total exposure value and
multiplies the pathway travel time by the number of commuters on the
pathway. It then weights the following parameters by the frequency of
occurrence of the case's wind speed, wind direction, and stability for
the emission time period (morning or evening): the number of commuters
on the pathway; the pathway commute-seconds; and the pathway exposure.
The first two weighted numbers are stored for the exposure class; the third
number is stored for use in computing the annual average pathway exposure.
The model then cycles to the next wind speed and repeats the procedure,
then to the next case (of the 480) for each wind speed, and so forth.
For each pathway, the weighted number of commuters is summed for each
exposure class, as are the weighted commute-seconds, and the weighted
exposures are summed. Finally, the percentage of commuters in each ex-
posure class is found by dividing the number in the class by the total
commuters; the percentages of commute-seconds (probabilities) are simi-
larly found. The annual average pathway exposure is the weighted expo-
sure sum.
If the user desires one or more subsets of the preceding information,
he may request it with model input. For example, if pathway "X" is of
particular interest, the user can input a flag and the pathway number to
produce either or both types of histogram data for pathway "X" only.
One other type of output is available when the model is in the annual
mode. If information about the variation with meteorology of exposure on
a particular pathway is desired, the user inputs a flag, the pathway number,
and the meteorological conditions of interest, and the model lists the
morning and evening exposures on the pathway for each combination of
98
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of meteorological parameters. If the meteorological conditions input
by the user do not occur (according to the frequency distribution), the
model will print a value of -99. for exposure.
5.3.1.3 Graphical OutputHistograms provide a convenient
means to depict the statistics generated by the model, especially since
such plots can be generated on a line printer. Due to incompatibilities
between makes of computers and between line printers, the code that gener-
ates such plots has not been included in the commuter exposure model.
However, all of the variables needed to plot histograms are included in
COMMON statements in a subroutine called GRAF. Definitions of the symbols
used as well as instructions on how to plot the histograms are also in-
cluded in subroutine GRAF as COMMENT statements. An input flag, IGRAF,
on card type 9M will cause GRAF to be called. Currently, it only prints
a message. The user is referred to subroutine GRAF and to the Glossary
of Symbols in the other volume of this package for aid in adding the
histogram-plotting capability to the model.
5.3.2 Uses and Interpretation of Output
The commuter exposure model can be used to analyze a number of air
quality problems as they relate to commuters. Regulatory agencies are
now showing increased interest in assessment of the daily or long-term
exposure to air pollutants of certain critical population groups. The
emphasis in air quality assessment is shifting away from the use of single-
point monitoring to define air quality and toward the analysis of the
pollutant exposure to which people, as moving receptors, are subject.
The population group subject to some of the highest exposure risk
99
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is commuters. Not only are the pollutant levels on the roadways over
which commuters drive high, but this group comprises a very large segment
of the population. The commuter exposure model is a tool that can be
used to assess the pollutant levels to which commuters are exposed in
various metropolitan areas. The model can be used to look at annual aver-
age exposures when it is run in the annual mode; when the user wants to
examine specific commutes, the model can be run in the short-term mode.
A single commute under worst-case traffic and meteorological conditions,
or an average single commute on an average single commute on an average
day, can be analyzed.
In either mode, the model output provides the user with a substantive
description of commuter exposure. Since the model treats the spatial
variation of exposure, regions of the city in which commuters experience
high exposures can be identified from model output. If a single commute
pathway is of interest, that pathway can be examined in detail.
The model facilitates study of exposure levels in relation to the
percentage of the commuting population exposed. The length of time of
exposure is also readily available for use in health effects studies.
In addition to being useful for determining absolute exposure levels,
the model can also assess the effects of implementing roadway improvements
or transportation control measures. The difference in commuter exposure
resulting from the various planning alternatives can be found by running
the model both without and with the proposed changes and then comparing
the computed results.
When interpreting the output of the commuter exposure model, the
user should bear in mind the assumptions made in defining the pathway network
100
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from the data base. The more thorough and complete the definition of the
network, the more reliable will be the results. The output of the model
can be no better than the data that was input: If, in a number of instances,
the user was required to estimate values for input parameters, note should
be taken as to the potential effects of such estimation on the output.
Another matter of importance regarding the interpretation of the
statistics generated by the model concerns the number of commuters treated
by the model. As discussed in our modeling methodologies report (Simmon
and Patterson, 1978) the commute pathways do not account for all of the
commuting in the modeled region. The number and complexity of the com-
muting routes in most metropolitan areas render a computer simulation of
the complete commuting picture nearly impossible. Given this initial
limitation for all cities, the model can nevertheless consider a larger
percentage of the total commuter population in some cities than in other
cities. The percentage of commuters treated by the model is dependent
not only on the size of the modeling area and the numbers of actual commut-
ing routes in the area, but also on the user's choice of routes and the
number of routes defined. For most cities, sufficient data are available
to enable the user to determine the total number of commuters in the area
and thus the percentage of the total commuters traveling on the defined
commute pathways. In interpreting model-produced statistics, the user
should remember that the statistics apply only to this percentage of the
commuters. In addition, the user knows that commuters traveling on the
pathways have, by definition, relatively long commutes, and hence risk
high exposure. By the same token, the commuters "skipped" by the suggested
method for pathway definition are those commuters with short home-to-work
101
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trips or having non-centralized places of employment. Thus, the commuter
exposure statistics developed by the model are biased toward high exposure
levels.
5.3.3 Model Limitations
Because of the complex nature of the commuter exposure assessment
problem, a model will of necessity have some limitations. The limitations
of the model detailed in this report are:
Not all commuters treated
Semiobjective method of commute pathway selection
» No seasonal or weekly traffic distribution
Constant acceleration and deceleration rates
Assumes annual average ambient air temperature
All morning commute vehicles assumed hot-running
Cold-start and hot-start percentages fixed for each locale
type for morning and evening commutes
Gaussian dispersion treatments cannot treat fumigation or
stagnation
Nonpathway dispersion treatment does not allow variation
of grid square emission rates
No provision for effects of precipitation
On-roadway and in-vehicle concentrations assumed equal.
In most instances, the commuter exposure model uses state-of-the-art
technology to simulate the traffic, vehicle emissions, and pollutant dis-
persion affecting commuters in an urban area. The areas in which improve-
ments to state-of-the-art techniques are needed are well known and have
been widely discussed. For example, the use of a Gaussian distribution
102
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to represent the variation in pollutant concentration usually assumes a
steady-state condition for periods of up to one hour in duration. Of
course, wind direction and speed and many other descriptive parameters
can vary considerably during such a time period. Thus, the assumption
of a steady-state is not always valid and (ideally) should be replaced
by a treatment that can accommodate high-frequency variations in basic
descriptive parameters. Limitations of this sort are well known, and
as the state-of-the-art progresses, they will be eliminated.
The model limitations discussed here relate to those assumptions in
the methodology that, while not ideal, are necessary to produce a prac-
tical, useful model of a reasonable size and moderate running costs. To
keep the modeling problem tractable, certain assumptions were necessary,
but care was taken that such assumptions would have a relatively minor
effect on the model output.
First, and most important, the model does not treat all commuters,
especially those with short commutes and commutes on less popular routes.
While ideally all commuters would be treated, the number of origin des-
tination zone pairs in a major metropolitan area needed to define all
commute routes is far greater than can be reasonably handled. Another
potential model limitation is the semiobjective method by which commute
routes are defined. A background familiarity with the area to be modeled
and experience in traffic modeling will undoubtedly make possible better
choices of commute routes. However, the suggestion that the local trans-
portation agency be contacted for help in selecting the commuting routes
somewhat mitigates this limitation.
To keep the calculation of annual average exposures within reason-
103
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able computational bounds, no peaks (other than diurnal) are allowed in
the traffic distribution. If the volume input to the model is annual
average daily traffic (AADT), the lack of peaking characteristics is not
a problem. However, if average daily traffic (ADT) is input, the lack
of weekly and seasonal peaks could affect the annual average exposures.
This effect can be alleviated by-adjusting ADT to AADT using seasonal
factors when computing annual average exposures. Another model assumption
is that the acceleration and deceleration rates have a constant value,
although the effects of this are expected to be minor. Single, constant
rates may be chosen that reflect the average emissions from a distribution
of rates.
Three assumptions are made relating to the computation of emission
rates. The assumptions are necessary to keep the number of model computa-
tions and the computer storage required at realistic levels. First, an
annual average ambient air temperature is used when the model is in the
annual mode. Next, in the morning, all vehicles on the commute routes are
assumed to be in a "warmed-up" mode of operation. (The vehicles reached
this state while traveling to the beginning of the commute route.) Finally,
when computing pollutant concentrations resulting from nonpathway sources,
fixed cold-start and hot-start percentages are assumed for the morning and
evening commutes for each locale type.
Four potential model limitations are related to the simulation of at-
mospheric dispersion. The use of the Gaussian dispersion formulations
presented precludes treatment of fumigation or stagnation conditions. The
Hanna-Cifford dispersion treatment used to compute concentrations result-
ing from nonpathway sources assumes that the emission rate in grid squares
104
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adjacent to the receptor square is the same as the emission rate in the
receptor grid square. Another factor which has been ignored in the com-
muter exposure modeling methodology is the effect of precipitation. Un-
doubtedly, some scavenging or rainout would occur. Finally, on-roadway
and in-vehicle concentrations have been assumed to be equal. The state
of the windows, air conditioner, heater, and so forth in the vehicle
would affect this relationship.
105
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-------
SECTION 6
SAMPLE APPLICATION
6.1 Overview
This section contains listings showing the results of PREPRS and
CEMAP applied to two commute pathways in St. Louis, Missouri. This sample
application is intended only as a simple example using real world data
rather than a comprehensive model application.
The listings in this section are divided into four parts. The first
is a listing of the input file used to run PREPRS; the second is the
output printed by PREPRS using the preceding input data. The third list-
ing is the input file used to run CEMAP in the sample application. Finally,
the listing generated by CEMAP, giving the model results, is included.
6.2 Data Sources
Several state and local agencies supplied the data necessary to run
the commuter exposure model for the St. Louis sample application. The
data and the agencies are identified below.
Traffic data for the sample application for St. Louis were obtained
from two sources: the East-West Gateway Coordinating Council and the
Transportation and Traffic Division for the City of St. Louis.
The East-West Gateway Coordinating Council is the comprehensive
planning organization for the St. Louis region. They were able to supply
maps of the major road transportation network for the region, and they
assisted in outlining the major commute pathways. Traffic volume maps
107
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showing ADT values were also obtained from this source, as were diurnal
distribution data, origin/destination data, land use information, and
vehicle miles traveled (VMT) figures.
The Transportation and Traffic Division was the source for intersec-
tion signal data for the CBD, as well as specific traffic count data col-
lected at various locations throughout the city.
The differences in the types of data obtained from these two agencies
should be noted, since the differences correspond to the basic functions
of each. The East-West Gateway Coordinating Council, as the comprehensive
planning organization, is responsible for large-scale planning and hence
develops and uses correspondingly large-scale network data. They are
knowledgeable about transportation patterns in the region. The Transporta-
tion and Traffic Division, on the other hand, must be concerned with basic
traffic movement and control, and hence is responsible for the design,
operation, and maintenance of the specific physical controls on traffic
movement.
Meteorological data for several sites in the St. Louis area were
obtained from GCA Corporation. Meteorological data for the airport ob-
servation site were obtained from the National Weather Service station
in St. Charles, Missouri.
The Division of Air Pollution Control, City of St. Louis, supplied
measured CO concentration data for five monitoring sites for the period
of interest.
108
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6.3 Input Data .Formulation
The data gathered from the agencies discussed in Section 2 were re-
duced by various means to a form compatible with the commuter exposure
model.
Reduction of the input data for the emissions preprocessor was rela-
tively straightforward. Model year and vehicle type distributions had
been obtained directly and were input to the model. Cold-start/hot-start
percentages were derived using the EPA guideline (Midurski and Castaline,
1977) for the determination of such percentages. The average ambient
air temperature used in the model was an average of the measured values
at five sites for the commute hour selected.
The intention of the sample application was to use actual data in
the model as a check to see that exposure levels of a magnitude that
might be expected were indeed predicted by the model. No attempt was made
to conduct a comprehensive modeling exercise, as-such a model applica-
tion could be a small project unto itself. Rather, the intent was to choose
a small number of pathways to model as a check and as an example.
Thus, two commute pathways were selected to be modeled. One of the
pathways contains one of the major commute freeways into the central
business district (CBD). It was divided into twenty segments. The other
pathway terminates in a secondary employment center, Clayton, and the
pathway consists primarily of arterial streets. It is represented by
four segments.
The segments on each pathway were determined by the existence of
major interchanges, changes in traffic volume, or changes in roadway
direction. Segments were numbered from the commute origin to its desti-
109
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nation. The coordinates of segment endpoints were derived from maps and
were measured in miles from an arbitrary grid origin. Traffic volumes
were taken from a network map showing bi-directional average daily traffic
i
(ADT) volumes. The model adapted the ADT to peak hour volumes internally
using the diurnal distribution data. Capacities were obtained from model
default values; speeds were computed by the model from capacity-restraint
relationships. Traffic signal data were taken directly from data supplied
by the local agency.
The number of commuters on the pathways were determined using average
daily traffic volumes, diurnal traffic distributions, length of the com-
mute, average vehicle occupancy rates, and overall guidance from origin/
destination data.
Diurnal traffic distributions were available and were used directly
to determine the percentage of average daily VMT that occurs on primary
and secondary streets during the commute hour that was modeled (0800-0900).
The ratios of secondary to primary street VMT by locale type were deter-
mined subjectively using network-wide averages and volume flow diagrams.
Grid square information was found by overlaying a grid system on
the volume flow map and manually tabulating the VMT in each grid square.
The length of each portion of a primary street passing through a grid
square was multiplied by the volume on that portion of the street. Such
products were summed to give the total VMT for each grid square. Locale
type was determined through a familiarity with the St. Louis area.
The meteorological data input to the model were derived from the
data listed in Table 1. The stations 1 through 5 are located in the City
of St. Louis and the near county. The airport station is somewhat more
110
-------
removed from the pathways that were modeled. The average wind speed,
wind direction, and temperature values used in the model were derived
giving more weight to data from observing sites near the pathways than
to the airport data. Neutral stability, class 4, was determined from
sky cover, wind speed, and time of day.
Table 10
METEOROLOGICAL DATA FOR ST. LOUIS SAMPLE APPLICATION DAY
13 MAY 1977, HOUR 0900
Station
1
2
3
4
5
airport
average used
in model
Wind
Direction
(Degrees)
180
195
Wind
Speed
(MPH)
1.7
3.8
212 ! 3.1
300
200
4 knots
1.3 m/s
Temperature
(Degrees F)
72.8
67.9
CO
(PPM)
13.6
2.5
74.3 : 5.5
70.9
72
72
0(?)
6.1
Opaque Sky
Cover (Tenths)
0
Stability
4
6.4 Discussion of Results of Sample Application
A listing of the input data for both parts of the model and the
results of the sample application are included on the'following pages.
The output of the emissions preprocessor is typical of emission factors
for carbon monoxide.
Ill
-------
Exposures computed by CEMAP are within the range of values that
would be expected. Total exposure and the average pathway concentration
on pathway 1 are greater than the corresponding numbers for pathway 2.
This is reasonable, since pathway 1 consists primarly of a major commute
freeway, while pathway 2 is representative of a less heavily traveled,
shorter commute route.
Pathway 1 terminates in the St. Louis CBD. The model computed a
background concentration of 12.6 mg/m^ (11 ppm) in that area. Of the
measured CO concentrations listed in Table 1, only one monitor is located
near either of the pathways modeled. That one is Station 1, which is
located in the CBD. The CO concentration measured at that location during
the commute being modeled was 13.6 ppm. Thus, it appears that the commuter
exposure model performed well in predicting the CBD background concentra-
tion.
The average concentration on pathway 1 was predicted at 15.6 mg/m
(13.7 ppm) and on pathway 2 at 9.28 mg/m^ (8.1 ppm). These values are
higher than the background levels that normally occur outside of the CBD
but are in keeping with the increased levels expected on the roadway it-
self, rather than nearby.
The model output includes warnings that the traffic volumes have
exceeded the listed capacity of some segments. These warnings are in-
cluded so that the user will know that the program has encountered in-
stances where adjustments in the volume were necessary for the program to
continue calculations and that the inputs should be checked for accuracy.
In addition to providing the histogram information about commuter
exposures, the model output also includes a breakdown of some of the in-
112
-------
formation used to generate the statistics. For example, the last page
of the output that follows gives a breakdown of the commuter exposures
arising from pathway and nonpathway sources, the travel time calculated
for the pathway and the average concentration along the pathway. The
last column also shows the concentration found for the last pathway seg-
ment, arising from nonpathway sources. This latter information is printed
only to provide a check to see if reasonable concentrations are being
generated near the path ending.
113
-------
PREPRS INPUT FOR SAMPLE RUN
(see Table 3)
Column Numbers
1
5
10
15
I
20
25 3
9 3* 4P 4?
50
55
60
65
70
75
80
IP ST LOUIS TEST 770S13 121 I C 2 2
2P ,02.09,10,10.09,06.08.09.OS.Ofe.OS.OS.04.02.02.02.01.010. 0. .80.13.02.04.010.
3P IS 15 0 72 02.5 -2.5
F IS 13 IS IS IS IS IS IS IS IS 0 'C 0 0 0
114
-------
PREPRS OUTPUT FOR SAMPLE RUN
ST LOUIS TEST
CAl. YEAR 1977 VEH. TYPE LOV LOT1 LOT2 HOG H30 MC
£«-02
15 8.074E+01
20 6.212E+01
25 5.052E*01
30 ^.Zl'.E + Ol
35 3.60bEf01
^0 3.200E+01
-------
PREPRS OUTPUT FOR SAMPLE RUN
(continued)
COMPOSITE EMISSION FACTOKS (GRAMS/MILE) FOR GRIODEO VMT
TEMPt*ATURE 72 (F)
AIR CONDITIONING 0 PC
SPEED 19.D MPH
LOCALE
TYPE
1
2
3
4
5
CARBON MONOXIDE
START MODE (PC)
COLD COLD HOT
CATALYST NON-CAT. CATALYST
15
15
15
15
15
15
15
15
15
15
0
0
0
0
0
EMISSION
FACTOR
7.264E+01
7.264E+01
7.264E+01
7.264E+01
7.2&4E+01
TEMPERATURE/COLD STARTS CCi<*ECTION FACTORS FOR CdO PATHWAY MJOAL EMIiSION FACTORS
TEMPERATURE 72 (F)
AIR CONDITIONING 0 PC
COLO STARTS IS PC (NON-CATALYST)
H3T STARTS 0 PC (CATALYST)
15 PC (CATALYST)
VEHICLE AGE
(YEARS)
1
2
3
4
5
6
7
a
9
10
11
12
13
14
15
16
17
ia
19
20
10
CARBON MONOXIDE
SPEED (MPH)
15 20
25
30
1.323
1.310
1.287
1.117
1.115
1.124
1.113
1.101
1.099
1.149
1.122
1.121
1.121
1.121
1.121
1.121
1.121
1.121
1.120
1.120
1.321
1.309
1.286
1.152
1.150
1.150
1.145
1.134
1.132
1.159
1.122
1.121
1.121
1.121
1.121
1.121
1.121
1.121
1.120
1.120
1.290
1.279
1.257
1.173
1.171
1.170
1.163
1.165
1.164
1.16ft
1.122
1.121
1.121
1.121
1.121
1.121
1.121
1.121
1.120
1.120
1.275
1.264
1.243
1.193
1.191
1.180
1.191
1.194
1.193
1.176
1.122
1.121
1.121
1.121
1.121
1.121
1.121
1.121
1.120
1.120
1.286
1.274
1.253
1.217
1.214
1.200
1.215
1.219
1.216
1.183
1.122
1.121
1.121
1.121
1.121
1.121
1.121
1.121
1.120
1.120
1.315
1.303
1.230
1.245
1.242
1.214
1.240
1.243
1.239
1.188
1.122
1.121
1.121
1.121
1.121
1.121
1.121
1.121
1.120
1.120
116
-------
PREPRS OUTPUT FOR SAMPLE RUN
(continued)
RATIO OF FTP COMPOSITE TO LOV EMISSION FACTORS FOR CORRECTING MODAL CBO PATHWAY EMISSIONS
TEMPERATURE 72 (F)
AIR CJNOITI3NIMG 0 PC
C3LO STARTS -15 PC (NJN-CATALYST)
H3T STARTS 0 PC (CATALYST)
15 PC (CATALYST)
SPEED (MPH)
5
10
15
20
25
30
CArfBJN MONOXIDE
RAT1J
1.076
1.121
1.124
1.121
1.113
1.111
AIR CONDITIONING CO«K£CTICH FACTORS FOR C80 PATHWAY MODAL EMISilO.N FACTORS
AIR CONDITIONING 0 PC
VEHICLE AGE
(YEARS)
1
2
3
<,
5
6
7
3
9
10
11
12
13
14
15
16
17
16
19
20
CARBON MONOXIDE
CORRECTION FACTOR
1.000
1.000
1.000
1.000
1.000
1.000
1.000
l.OCO
1.000
1.300
000
000
000
000
000
000
000
000
000
000
117
-------
PREPRS OUTPUT FOR SAMPLE RUN
(concluded)
COMPOSITE CXU1SE (GRA.1S/SEC) AND EXCESS (G*AMS) EMISSION FACTORS FOR CBO PATHWAY VMT
TEMPERATURE 72 (F)
AIR CONDITIONING 0 PC
COLD STARTS 15 PC (NON-CATALYST)
HOT STARTS 0 PC (CATALYST)
15 PC (CATALYST)
SPtEO
-------
CEMAP INPUT FOR SAMPLE RUN
(see Table 3)
Column Numbers
1 5 10 15 20 25 30 35 40 45
i i i > i i i i i |
1» ST LOUIS TEST 770513 2111
2« 220 4 201.
3*
3M
3*
3*
3*
3»
3M
3"
3M
3*
3*
3M
3*
3H
3M
3W
3<*
J»
3*
11120340.20343.
21132185.32185.
31133700.33700.
4| 13481 5.34815.
51138745.36745.
61 141 56). 4 1560.
71141560.41560.
8114203S.4293S.
911 44415. 44415.
101152660. 5266 C.
1II14S510. 45510.
1211 46075.46075.
131 142880.42683.
141143985.40905.
151 1Z3I6S.261 85.
161129075.29075.
1713 300. 300.
1813 444. 444.
1913 2020. 2020.
3« 12012 529. 529.
3* 2112 9260. 9260.
3" 2 212 9260. 9260.
3" 2 312 8485. 6465.
3M 2 412 4809. 4809.
-
-
-
-
-
-
»
«»
-
-
-
*»
«
-
«
-
-
-
-
-
-
.
-
02212
02212
02212
02212
02212
02212
02212
02212
03312
03312
03312
03312
03312
03312
04412
04412
-0 02212
-9 02212
-0 02212
-0 02212
-1 02212
-1 02212
-1 02212
-1 02212
50
sess
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
55
«
5
7
8
9
10
to
11
12
13
14
IS
60
.33
.67
.00
.88
.38
.00
.58
.79
.67
.29
.63
.17
15.92
17
18
19
20
20
20
20
13
13
13
13
.06
.75
.92
.46
.42
.56
.67
.63
.63
.46
.29
65
3.67
3.67
3.67
3.4«
3.46
3.50
3.21
3.04
3.04
3.00
3.00
3.29
3.21
3.17
3.25
2.92
2.6.3
2.33
2.29
2.74
7.86
6.79
5.96
4.46
70
5.67
7.00
8.88
4.36
10.00
1 0.58
11.79
12.67
13.29
14.63
15.17
15.92
17.08
18.75
19.92
20.46
20.42
20.58
20.67
20.75
13.63
13.46
13.29
13.21
75 80
3.670
3.670
3.460
3.460
3.500
3.21 0
3.040
3.040
3.000
3.000
3.290
3.210
3.170
3.250
2.920
2.63C
2.3321
2.2921
2.7421
2.920
6.790
5.960
4,460
4.460
4« 117150.ISO.150.150
4M 118222.222.222.222
4M II9101010I010I0101
SM 5100 14SO
.15001500150015007^.64.46.35.
.1SOO1SC01SOOIS0010OS3.72.3S.
0150015001500150010010072.52.
119
-------
CEMAP INPUT FOR SAMPLE RUN
(concluded)
Column Numbers
1
i
6V
7M
7V
7M
7*
7V
7v
7V
7v
7M
7V
7V,
7M
7M
7V
7V.
7V
7M
7V
7V
7V
8V
5 10
t i
90.
1
2
3
4
5
6
7
a
9
10
11
12
13
14
15
16
17
18
14
20
1.3
15 20
i i
.50 .20
5.0
6.0
7.0
8.0
9.0
10.0
1 1.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
13.3
13.3
13.0
13.0
200 4
25 39
.15 .05
3*01.0
3.01 .0
3.01.0
3.01.3
3.01 .0
2.61 .0
2.51 .3
2.51 .0
2.51.0
2.51 .0
2.51 .0
2.51.0
2.51.0
2.51.0
2.51 .0
2.251 .0
7.01.0
6.01 .0
5.01.0
4.01.0
35 40 45
t i i
7C31
76COC
25430
15527
54268
15184
2696
52638
40460
45£7C
42930
70258
87286
94579
84775
103511
15441
46083
13905
74782
50
5
c
3
3
3
3
3
2
2
2
2
4
4
4
1
1
3
3
3
2
120
-------
CEMAP INPUT FOR SAMPLE RUN
(concluded)
Column Numbers
i
6V
7M
7m
7m
7m
7m
7m
7m
7m
7M
7*
7M
7M
7m
7m
7m
7m
7M
7»
7m
7m
9m
5 10
i i
90.
t
2
3
4
5
6
7
8
9
to
II
12
13
14
IS
16
17
18
19
20
1.3
15 20
i i
.SO .20
S.O
6.0
7.0
8.0
9.0
10.0
11*0
12.0
13.0
14.0
IS. 0
16.0
17.0
18.0
19.0
20.0
13.3
13.3
13.0
13.0
200 4
25
.IS .
3.01
3.01
3.01
3.01
3.01
2. 81
2.51
2. SI
2. SI
2.51
2.51
2. SI
2.51
2.51
2.51
2. 251
7.01
6.01
5.01
4.01
39 35
OS
.0
.0
.0
.3
.0
.0
.3
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
40 45
7C3I
7«COC
25430
15S27
S4260
15184
2t«
52638
40460
45*70
42930
70256
87286
9*579
64775
1035! 1
15441
46063
1390S
74783
50
S
S
3
3
2
3
3
2
2
2
2
4
4
4
1
I
3
3
3
2
121
-------
CEMAP INPUT FOR SAMPLE RUN
(concluded)
Column Numbers
1
61*
7*
7*
7*
7*
7*
7*
7*
7*
7*«
7W
7*
7*
7M
7*
7*
7M
7*
7M
7V
8*
5 10 15 20
till
90.
1
2
3
4
5
6
7
a
9
10
11
12
13
14
15
16
17
18
19
20
1.3
.50 .20
5.0
6.0
7.0
6.0
9.0
10.0
1 1.0
12.0
13.0
14.0
15.0
16.0
17.0
ia. o
19.0
20.0
13.3
13.3
13.0
13.0
200 4
25 30 35
.15 .05
3.01.0
3.01 .0
3.01.0
3.01.3
3.01 .0
2.81 .0
2.51 .0
2.51 .0
2.51.0
2.51.0
2.51.0
2.51.0
2.51 .0
2.51.0
2.51 .0
2.251 .0
7.01.0
6.01.0
5.01.0
4.01.0
40 45
7C31
76COC
25430
15527
54266
15184
2696
52636
40460
45670
42930
70256
87286
94579
64775
103511
15441
46063
13905
74762
50
5
5
3
3
3
3
3
2
2
2
2
4
4
4
1
1
3
3
3
2
122
-------
CEMAP INPUT
o-
00
°"* W *l ^ %j W ^^ W *-* ^ v/ i^ v^ *-r ^J ^^ ^ x^ W
-------
CEMAP OUTPUT
S -
a
4
X
9
*
U
O
a
tt O 9
a < IM
o - j in
ik
du in
* u» - «
o j | ^ c
a - m ik w
*i o z
u iu v! lu 4 u) e
" o - v t e £
) r» X T « to ~
a M a > to z
J ~ Ik < <* Ik Ik C
» a 5 o o
to W«9ZI
M toa
« j < o e > <
to XOOOOOOOOOOOOOOO««»»OOOOO
W « I I I I I I t I I I I I I I I I I I I I I
ttJ U
to 0. OOOOOOOOOOOOOOOONMNOOOOO
to
OOOOO9OOOOO9OOO9OOOO9OOO
1(1 .2N*0>Ii-*^»5«to^*f?^0*'**^'*'*
to
<
z
a 0000000000*90000000000000
a
a
z u
a
to
Ifl
& AiOOO'')OiAK 00000000^0900^0000000000
o Sr a
U X <
z u
U Ik Z
111 u. O «. i-
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ik a
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to U
«
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< z -- ------------0000----
u a < a i i i i i i i i i i i i i i i i i i i i i i i i
to in
Ik <
i e I rT777TTT77T7TT77Trr777TT
o a ooooooooooooooooooooaooo
z a
UI
to
I OL
to n v
s *
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u
V
z
to MMM^MMMMfltMMMMMwMMWW^AII
<
124
-------
CEMAP OUTPUT
HI
a
*
o
IU
u
IX
3 tl 'fl IV
O » 1 O
Z >
- «
2 »
c c
O
o
2900
K O O
J
a *> IM -
a - (M o
o -
i
a
V O 10 O
N» n N «
3 IM a
a -
in . , .
2O IM O
J1 M -
a IM o
3 -
X . . I
< O N O
V «! - o o o
3 o ni «
o - - -
c o o o
a o o o
a & f m
o *
i . . .
< o a e
s o o o
z J) o «i
3
a
z
o
u
a
3
o
a
z
>
a o o o o o
< o o o m in
e in M - o
o a
a
c
- o
I
<
a
-------
CEMAP OUTPUT
ooQoooooaooeoaeoaooe
u. u; u v u u. u u' u u u u. u; u. u: u u; u; u. VL
ooeeooooooeiooeoeoono
o «u oooooooouoooobeooooo
tn
1
*
> u
N U
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^
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in
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to >
a.
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(rt ^
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3
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z
u
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a -
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c a.
x u
to
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in
a
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^
^ (U
u
X
z
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to
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X
a -
u
x u
to
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in
3
a.
u
^ Ul
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*
*
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^ a
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to
x z
< o
z
a -
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z <
c a
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to
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t
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3
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» u
u
g
2
3i
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a
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to
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3
a
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g
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to
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£
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to
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in
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in
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1
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z
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to
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-------
REFERENCES
Benson, P.E., CALINE3 - A Versatile Dispersion Model for Predicting Air
Pollutant Levels Near Highways and Arterial Streets, Interim Report,
California Department of Transportation, Sacramento, California 1979.
Hanna, S.R., A Simple Method of Calculating Dispersion from Urban Area
Sources, J. Air Pollution Control Assoc. 21, pp. 774-777, 1971.
Highway Capacity Manual, 1965. National Academy of Sciences, Highway
Research Branch, Special Report No. 87.
Johnson, W.B., W.F. Dabberdt, F.L. Ludwig, and R.J. Allen, 1971.
"Field Study for Initial Evaluation of an Urban Diffusion Model
for Carbon Monoxide," Comprehensive Report CRC and Environmental
Protection Agency (EPA) Contract CAPA-3-68 (1-69), SRI International,
Menlo Park, California.
Ludwig, F.L., and W.F. Dabberdt, Evaluation of the APRAC-IA Urban Dif-
fusion Model for Carbon Monoxide, Final Report, CRC and EPA Contract
CAPA-3-68 (1-69), SRI International, Menlo Park, California, 1972.
Ludwig, F.L., P.B. Simmon, R.C. Sandys, J.C. Bobick, L.R. Seiders, and
R.L. Mancuso, User's Manual for the APRAC-2 Emissions and Dispersion
Model, Final Report, EPA Contract No. 68-01-3807, SRI International,
Menlo Park, California, 1977.
Metropolitan Washington Council of Governments, "Existing Transportation
Systems in the Washington Metropolitan Area," 1976.
Midurski, T.P., and A.H. Castaline, 1977. "Determination of Percentages
of Vehicles Operating in the Cold Start Mode," Contract No. 68-02-1376,
Environmental Protection Agency, Research Triangle Park, North
Carolina.
129
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Mobile Source Emission Factors, Final Document, Environmental Protection
Agency, Office of Transportation and Land Use Policy, Washington,
D.C., 1978.
Modal Program Guide, an update to Automobile Exhaust Emission Modal Analy-
sis Model, U.A. EPA Report No. EPA-460/3-74-005, 1974.
Patterson, R.M., Air Quality Modeling at Signalized Intersections, Con-
ference on State of the Art of Assessing Transportation-Related
Air Quality Impacts, Transportation Research Board, National
Academy of Sciences, Washington, D.C., October 22-24, 1975.
Simmon, P.B., and R.M. Patterson, Commuter Exposure Modeling Methodologies,
Report No. EPA-600/4-79-010, U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina, February 1979.
Simmon, P.B., F.L. Ludwig, R.M. Patterson, and L.B. Jones, User's Manual
for the APRAC-3/MOBILE1 Emissions and Diffusion Modeling Package,
Final Report, EPA Contract No. 68-02-2548, SRI International,
Menlo Park, California, 1981.
130
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
2.
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
COMMUTER EXPOSURE MODEL
User's Guide
5. REPORT DATE
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
P. B. Simmon and R. M. Patterson
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
SRI International
Menlo Park, CA 94025
10. PROGRAM CLEMENT NO.
CDTA1D/04-0500 (FY-82)
11. CONTRACT/GRANT NO.
68-02-2981
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Sciences Research LaboratoryRTP,NC
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final 10/78 - 4/81
14. SPONSORING AGENCY CODE
EPA/600/09
15. SUPPLEMENTARY NOTES
16. ABSTRACT
The commuter exposure modeling package consists of two documents. This
volume is the User's Guide. It describes program execution and provides the
user with the information needed to run the program. The other volume provides
a detailed description of the model methodology and code. In this guide, the
potential uses of the model are discussed and a brief overview of the modeling
methodology is presented. A comprehensive section describing the details of the
implementation of the model methodology on a computer and the associated computer
requirements is included.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COSATI Field/Group
8. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY
(This Report)
21. NO. OF PAGES
139
20. SECURITY CLASS (Thispage)
22. PRICE
EPA Form 2220-1 (t-73)
131
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