EPA-450/3-74-003-a
August 1973
VEHICLE BEHAVIOR
IN AND AROUND
COMPLEX SOURCES
AND RELATED COMPLEX
SOURCE CHARACTERISTICS
VOLUME I - SHOPPING CENTERS
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Water Programs
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
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VEHICLE BEHAVIO
IN AND AROUND
COMPLEX SOURCE!
SOURCE CHARACTERISTIC!
VOLUME I - SHOPPING
by
Scott D. Thayer and Kenneth Axetell, Jr
Geomet, Inc.
50 Monroe Street
Rockville, Maryland 20850
Contract No. 68-02-1094
Task Order No. 1
EPA Project Officer: Edwin Meyer
Prepared for
ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Water Programs
Office of Air Quality Planning and Standards
Research Triangle Park, N. C. 27711
August 1973
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This report is issued by the Environmental Protection Agency to report
technical data of interest to a limited number of readers. Copies are
available free of charge to Federal employees, current contractors and
grantees, and nonprofit organizations - as supplies permit - from the
Air Pollution Technical Information Center, Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, or from the
National Technical Information Service, 5285 Port Royal Road, Springfield,
Virginia 22151.
This report was furnished to the Environmental Protection Agency by
Geomet, Inc., 50 Monroe Street, Rockville, Maryland, in fulfillment
of Contract No. 68-02-1094. The contents of this report are reproduced
herein as received from Geomet, Inc. . The opinions, findings, and
conclusions expressed are those of the author and not necessarily those
of the Environmental Protection Agency. Mention of company or product
names is not to be considered as an endorsement by the Environmental
Protection Agency.
Publication No. EPA-450/3-74-003-a
ii
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ABSTRACT
The report presents a general methodology for interpreting parameters
which characterize a complex source into descriptions of traffic behavior
in and around the source. The methodology is implemented in quantitative
fashion for the first of seven types of complex source, regional shopping
centers; the information generated, relating shopping center parameters
to the associated traffic behavior, will now be used by the sponsor to
generate guidance for studying .he impact of new shopping centers on air
quality.
111
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CONTENTS
Page
Abstract iii
List of Figures V
List of Tables vl
Sections
I Conclusions 1
II Recommendations 2
III Introduction 3
IV Characteristics of Shopping Centers 7
V Regional Shopping Center Parameters 9
VI Traffic Parameters, Values and Derivations 19
VII Analysis 26
VIII Results 36
X Data Sources 51
Appendix A 53
Appendix B 58
iv
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FIGURES
No. Page
1 Montgomery Mall Shopping Center 11
2 Landmark Center 12
3 Schematic Representation of Vehicle Operating Modes at a 21
Shopping Center
4 General Relationship Between Traffic Volume and Total Running 24
Time
5 Generalized Methodology 37
6 Generalized Methodology Applied to Shopping Centers 38
7 Isopleths (m sec" ) of mean autumn wind speed averaged through 45
the afternoon mixing layer
8 Isopleths (m sec" ) of mean winter wind speed averaged through 46
the afternoon mixing layer
2
9 Isopleths (m x 10 ) of mean autumn afternoon mixing heights 47
p
10 Isopleths (m x 10 ) of mean winter afternoon mixing heights 48
B-l Traffic Impact Study Saturday Hourly Variation of Traffic 60
Entering and Leaving Montgomery Mall
B-2 Traffic Impact Study Weekday Hourly Variation of Traffic 59
Entering and Leaving Montgomery Mall
B-3 Traffic Impact Study Saturday Hourly Variation of Traffic 64
Entering and Leaving Landmark
B-4 Traffic Impact Study Weekday Hourly Variation of Traffic 63
Entering and Leaving Landmark
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TABLES
No. Page
1 General Characteristics of the Three Major Types of Shopping 7
Centers: Regional, Community, Neighborhood
2 Greater Washington Regional Shopping Centers 8
3 Parking Space Data for Greater Washington Regional Shopping 14
Centers
4 Summary of ULI Data on Parking Spaces in Regional Centers 15
of Various Sizes
5 Summary of Trip Generation Data (one-way) for Regional 16
Shopping Centers from COG Report
6 Seasonal Demand for Shopper Parking 17
7 Vehicle Exhaust Emissions at Idle in Grams per Minute 20
8 Base Running Times by Operating Mode at Two Suburban 23
Washington, D.C. Shopping Centers
9 Trip Generation Data from the COG Report, and Suggested 28
Values for Use, as a Function of Regignal Shopping Center Size
10 Example Queue Calculation when Gate Capacity is Exceeded 32
11 Key to Stability Categories (after Turner 1970) 43
B-l Total Traffic Entering and Leaving the Shopping Center During 58
Average Weekday and Saturday
B-2 Relative Use of Access Points at the Shopping Center for an 58
Average Weekday and Saturday
B-3 Total Traffic Entering and Leaving the Shopping Center During 62
Average Weekday and Saturday
B-4 Relative Use of Access Points at the Shopping Center for an 62
Average Weekday and Saturday
v1
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SECTION 1
CONCLUSIONS
1. A general methodology has been developed which permits relating
parameters descriptive of traffic behavior associated with developments
(complex sources) to the available descriptive characteristics of the
complexes themselves. These relationships are subsequently to be used
by the sponsor to develop guidance for relating the complex's characteristics
to air quality.
2. The methodology has been successfully applied to the first (shopping
centers) of seven types of complexes, with quantitative results presented
in this task report.
3. It is now appropriate to proceed to the next type of complex (sports
complexes), and apply the methodology appropriately.
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SECTION II
RECOMMENDATIONS
It is recommended that, as planned, the project officer employ this
methodology to develop guidance for relating the traffic characteristics
of shopping centers to typical and peak air pollution concentrations.
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SECTION III
INTRODUCTION
INTRODUCTION
OBJECTIVE AND SCOPE
The ability to estimate traffic characteristics for proposed developments
and the resulting effects on air quality is an important prerequisite for
promulgating State Implementation Plans which adequately address themselves
to the maintenance of NAAQS. Prior to estimating the impact of a develop-
ment (complex source) on air quality, it is necessary tyat traffic charac-
teristics associated with the source be identified and related to parameters
of the development which can be readily identified b.y the developer a priori,
The purpose of this study is to identify traffic characteristics associated
with specified varieties of complex sources and to relate these character-
istics to readily identifiable parameters of the complexes. The end
product of this task will then be used to develop an /\1n Pollution
Technical Document which will provide guidance to enable,
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This, the first task report, describes the methodology developed, and
the analysis and results of its application to shopping centers.
APPROACH
Due to internal constraints, the sponsor has been forced to impose a tight
schedule on this project, permitting only two to three weeks for the analysis
and reporting of each sub-task. Accordingly, the employment of readily
available traffic design information for each type of complex has been
suggested as the general approach.
The approach was designed to permit the development of answers to the
following questions posed by the sponsor, using available traffic design
and behavior data, and available data on parameters of the complex:
1. How much area is allotted or occupied by a single motor vehicle?
2. How much or what percentage of the land occupied by the complex source
(and the source's parking facilities) can potentially be occupied by vehicles?
What is the usual percentage?
3. What portion of the vehicles within the complex are likely to be
running at any given time during a 1-hour period? During an 8-hour period?
We are interested in both peak and typical circumstances here.
4. What is the typical and worst case (slowest) vehicle speed over 1-hour
and 8-hour periods?
5. How are moving and parked vehicles distributed within the complex
property? (e.g., uniformly?)
6. What are the design parameters for each type of complex which are
likely to be known by the prospective developer beforehand?
7. Which ones of the design parameters in number 6 can be most successfully
related to traffic and emissions generated by the complex? What is the
best estimate for relationships between readily obtainable parameters and
emissions?
8. What are the relationships of parking "lot" design to parking densities
and vehicle circulation? What represents a typical design and/or a design
which has highest parking densities, lowest vehicle speeds, longest vehicle
operating times?
9. What meteorological conditions r(i.e., atmospheric dilutive capacity)
are likely to occur during periods of peak use? What use level is likely to
occur during periods of worst meteorology (i.e., atmospheric dilutive capacity?)
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The technical approach developed and implemented in this report consists
of, first, structuring a methodology for describing engine operating modes
which considers both the principal modes in automobile operation in and
around complexes, and the emission significance of each mode. In our analysis
this leads to an important emphasis on engine operating time, with only
secondary significance attached to operating speed and distance.
For the complex being studied, an analysis is made of the typical movements
of vehicles, and their movements under conditions of congestion, caused by
peak traffic loads or by awkward design elements of the complex, or both.
This highlights the traffic operational modes which have greatest effect on
running times, and assists in seeking out the elements or parameters of
the complex which influence these running times most.
The running times in critical modes are found to be dependent on the usage
rate of the complex as a percent of capacity. In addition, absolute values
of usage as a function of time are needed as a direct input for estimating
emissions. Therefore, data on usage patterns of the complex by season,
day of the week, and hour of the day are collected and related to capacity
parameters. The results are used in two important ways:
1. To develop quantitative relationships between running times and various
percent-usage parameters; and
2. To provide general usage patterns from which the usage pattern for
a complex of interest can be inferred, if no measured data are available.
Basic parametric values are then derived which define typical base line
running times and use rates; these are used both to provide a point of
departure for the peak case calculations, and as input to the estimate of
typical conditions.
For any parameter of capacity (parking, entrance, exit), resulting increases
in running time for each mode are estimated as they may be functions of the
exceedance of that capacity. The base running time is then used in conjunction
with typical use rates to generate typical combinations of running times and
numbers of vehicles running. Finally, peak (1-hour and 8-hour) use rates
are compared to capacities in order to calculate, using the above derived
functionalities, the associated peak values of number of vehicles running,
and running times.
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It may often be possible, in addition, to develop and provide qualitative
guidelines which can provide further insight into factors which may aggravate
or alleviate congestion. These are provided separately from the quantitative
relationships.
Finally, the meteorological conditions associated with the occurrence of
the peak "(v.ehicle number ) (running time)" values are defined; in addition
periods of the most adverse meteorological conditions are determined, and
the use rate data examined to determine associated use rates and running
times.
The methodology is considered to be completely general, and to apply to
all the complex sources of concern here, with the exception of "major
highway" case cited in the section titled Objective and Scope. That special
case is recognized in the work statement as an unusual one requiring different
treatment in the context of the other six sources. In any event, and in the
words of that statement, "for highways it may simply be necessary to tie
existing guidelines into a concise package."
The remainder of the report describes the implementation of this methodology
for shopping centers, and the results obtained.
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SECTION IV
CHARACTERISTICS OF SHOPPING CENTERS
The literature and practice of architecture and development widely
recognizes that shopping centers may logically be divided into three
principal categories: regional, community and neighborhood. These
differ importantly from each other in major characteristics, and are so
divided for a variety of purposes. Major differences are shown in
Table 1.
Table 1. GENERAL CHARACTERISTICS OF THE THREE MAJOR TYPES OF
SHOPPING CENTERS: REGIONAL, COMMUNITY/NEIGHBORHOOD
Type
Size
(gross
leasable
floor
space)
Total
acreage
Major
stores
Population
served
Usual park-
ing lot
type
Order of
magnitude
of average
shopper
stay
Regional
Average 400,000
sq.ft.; (300,000-
1,000,000 and up)
>, 30 acres
One or two major
dept. stores
^ 150,000
open, peripheral
2 hours
Community
Average 150,000
sq.ft.; (100,000-
300,000 sq.ft.)
10 to 30 acres
Variety, or junior
dept. store, and
supermarket
40,000-150,000
open, one side
45 minutes
Neighborhood
Average 50,000
sq.ft.; (30,000-
100,000 sq.ft.)
4 to 10 acres
Supermarket
5,000-40,000
open, one side
15 minutes
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Because of the major physical differences in the center types, and known
differences in parameter values (parking capacity and use rate, e.g.), it
was necessary to select one of the three types of center for data collect-
ing and analysis, in the limited time available. The choice (regional
centers) was obvious because of the large size and use rates involved,
and was supported by the precedent of the Federal Register (Vol. 38,
No. 116).
GREATER WASHINGTON REGIONAL SHOPPING CENTERS
All of the regional shopping centers in the Greater Washington area are
listed in Table 2, along with data indicating the range of size represented
by these twelve centers.
It is evident that a wide range of examples of the regional center exists
in and near Greater Washington; further, these cover a variety of basic
designs, and ages; our use of the body of information associated with
examples from these centers, along with other more general data, would
therefore be expected to not only apply to this locality, but also to be
oroadly applicable to regional centers in other parts of the county.
Table 2. GREATER WASHINGTON REGIONAL SHOPPING CENTERS
Name
Tyson's Corner
Landover Mall
Wheaton Plaza
Springfield Mall
Prince George's Plaza
Beltway Plaza
Montgomery Mall
Landmark Center
Iverson Mall
Columbia Mall
Seven Corners Center
Rockville Town Center Mall
Total Size
90 acres
88 acres
80 acres
88 acres
52 acres
70 acres
55 acres
52 acres
23 acres
40 acres
38.5 acres
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Building
Square Footage
1 ,500,000 sq.ft:
1,350,000
1,250,000
1,200,000
872,000
800,000
729,000
665,000
640,000
625,000
597,000
500,000
Number
Stores
120
125
75
130
81
75
60
36
72
102
48
29
Annual Sales
$120,000,000
.__
$100,000,000
$ 50,000,000
$ 60,000,000
$ 90,000,000
$ 52,000,000
$ 40,000,000
$ 48,000,000
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SECTION V
REGIONAL SHOPPING CENTER PARAMETERS
In the following discussion of shopping center parameters and values, we
emphasize those which are commonly known to, and used by, developers in
the course of their work. In addition, other parameters are coming into
use, more specifically oriented toward assessing the air pollution impact
of the traffic and other emission sources associated with complex sources.
An example of this increasing awareness and concern is found in Appendix A,
copy of which was provided to us by the Maryland Bureau of Air Quality
Control. This is an extract from a procedure proposed by the International
Council of Shopping Centers as a model code and administrative procedure,
for reviewing complex sources, which embodies the regulatory requirements
related to state preparation, adoption and submittal of air quality imple-
mentation plans. This, we believe, represents a growing tendency of
developers to attempt to anticipate the requirements of impact analysis
for complex sources.
Customarily, the developer has a variety of sources of information avail-
able to him, some of which are in the form of constraints (e.g., zoning
regulations) and others comprise information sought out by the developer
to support his decisions (e.g., marketing analyses, demographic surveys,
characteristics of the population to be served, and trip generation
information or estimates). The information presented in this section
involves that which is both available and applicable to the problem.
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SIZE
The size of a center is most often given in terms of square footage of
leasable store space, although total acreage and number of stores are
sometimes useful indicators of size for various purpose. The store square
footage is the most useful indicator for our purposes, as will be seen
subsequently^
Specifics of regional center size will be presented subsequently in the
discussion of parking spaces (see section titled Parking Spaces); hence
only general comments are given here.
Regional centers range in size from some 300,000 sq. ft. in shopping area
to the order of 1,500,000 sq. ft. with total size from around 30 to around
100 acres, or approximately 1 million to four million square feet. The
number of stores can range from several dozen up to well over a hundred.
LAYOUT, BLUEPRINT OR SCHEMATIC DIAGRAM, AND RELATED DATA
Examples of this type of information, which is necessary for analysis of
running times and gate use, are given in Figure 1 for Montgomery Mall,
and Figure 2 for Landmark Center. In addition to the general layouts,
gates, internal flow (which can be inferred or observed), and nature and
characteristics of the access raods, all of which are implicitly or
explicitly available from such diagrams, corollary data should be obtained
or derived on gate capacitites, gate traffic controls, and their character-
istics, and distribution of traffic volumes and densities on access roads.
PARKING SPACES
Parking space data represent vitcl parametric characteristics for any
complex source, and regional shopping centers are no exception. A para-
meter frequently used is that of parking spaces (for customers) available
per thousand square feet of leasable store space. As a matter of incidental
information, the generally accepted range of space allotted for parked cars
is from 9 x 20 feet (180 sq. ft.) to 10 x 20 feet (200 sq. ft.).
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Note:
Five "X"4s locate all
major entrances and
exits; secondary gates
give access to gas station
on west corner and offices
on north corner, which have
access to streets.
Figure 1.
MON TGOME K Y
SHOPPING ce
M A I I
N T £ t
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C/E THfc J OWE
Figure 2.
LanomarK cenreR
Ps PARKING
5839 DUKE STREET ALEXANDRIA. VIRGINIA.223O4
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Values encountered, for the critical parameter of parking spaces per
thousand square feet of selling area, can range from as low as three for
the older, high parking density centers (which are generally smaller, as
well) through many centers in the five to six range, and ocassionally up
to seven, eight or even nine. Seven is indicated by Baker and Funaro* to
be a "desirable parking index," while the Urban Land Institute states
that, based on a study of peak parking at 270 centers, 5.5 spaces per
thousand square feet is adequate as a standard for meeting all but the
10 highest hours of parking demand during an entire year. There are many
obviously conflicting requirements regarding this parameter - our primary
interest is in expected values, ranges of values, and their influence on
traffic.
Table 3 expands the data previously given for Greater Washington Regional
Centers to now include data on parking capacitites as well. We see that,
of the 11 centers for which parking space data could be obtained, six
ranged between five and six spaces per 1000 square feet; three ranged
between about 4.5 and 5.0, and two below four.
Additional extensive data on parking capacity is included as part of the
Urban Land Institute's study on parking requirements for shopping centers.
This is summarized in Table 4. In this table, the relative invariance of
the reported mean values with center size (except for a slight tendency
to decrease at the highest center sizes) is interesting, especially in view
of the rather extreme ranges found at almost all center sizes. The median
values are largest near the middle range of center sizes (500,000 to
900,000 sq. ft.), and are smaller than this peak for both smaller and
larger centers.
TRIP GENERATION
The most prevalent and useful term for describing traffic activity associated
with shopping centers is "trip generation." This term is defined by traffic
engineers as follows: a trip usually refers to a single vehicular move-
ment (one-way) having either an origin or a destination within the area or
site of interest. Generation usually refers to the total number of trips
* See Data Sources Section
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Table3. PARKING SPACE DATA FOR GREATER WASHINGTON REGIONAL SHOPPING CENTERS
1 , Tyson's Corner
2. Landover Mall
3. Wheaton Plaza
4. Springfield Mall
5. Prince Georges Plaza
6. Beltway Plaza
7. Montgomery Mall
8. Landmark Center
9. Iverson Mall
10. Columbia Mall
11. Seven Corners Mall
12. Rockville Mall
Total
Acres
90
88
80
88
52
70
55
52
23
40
38.5
_**
Total
Sq. Ft.
3,920,400
3,833,280
3,484,800
3,833,280
2,265,120
3,049,200
2,395,800
2,265,120
1 ,001 ,880
1 ,742,400
1,677,060
_**
Store
Sq. Ft.
1,500,000
1,350,000
1,250,000
1,200,000
872,000
800,000
729,000
665,000
640,000
625,000
597,000
500,000
Nr
Stores
120
125
75
130
81
75
60
36
72
102
48
29
Parking
Spaces
6600
6200
6000
*
4926
4000
4000
3910
3500,.
2300
3000
1960
Parking Spaces
per 1000 sq.ft.
of store space
4.4
4.6
4.8
*
5.65
5.00
5.49
5.88
5.47
3.68
5.03
3.92**
Stores 0/
Total h
38.3
35.2
35.9
31.3
38.5
26.2
30.4
29.4
63.9
35.9
35.6
-
*Being repaved and resurfaced - number unknown.
** Unusual design for regional center enclosed two-story parking under mall.
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Table 4. SUMMARY OF ULI DATA ON PARKING SPACES IN
REGIONAL CENTERS OF VARIOUS SIZES
Center
Size Range
Thousands of sq. ft.
300-399
400-499
500-599
600-699
700-799
800-899
900-999
1,000-1,250
Total
Number of
Centers
33
21
10
12
4
3
5
5
93*
Parking Spaces per
Median
5.95
6.31
7.12
6.92
6.48
7.19
5.90
5.41
Mean
6.59
6.70
6.77
6.61
6.57
6.51
6.16
5.67
1000 sq.ft.
Range
2.66-11.45
3.66-10.01
4.09^8.55
4.46-9.41
5.66-7.66
5.03-7.30
5.58-7.16
4.32-7.92
*The remaining 177 (total 270) centers were less than 300,000 sq. ft.
in size, and thus not defined as regional centers.
within a given time period crossing a counting station. These trips can
have either an origin or a destination within the site, and are ordinarily
expressed as rates; for regional shopping centers, we quantify trip
generation in numbers of trips generated per thousand square feet of store
area.
A summary of some available data on trip generation for regional centers
is presented in Table 5. This was obtained from a data review performed
by the National Capital Region Transportation Planning Board of the
Metropolitan Washington Council of Governments (COG), hereafter referred
to as the COG report.
We also require data on seasonal, weekly, and hourly variations in trip
generation rates in order to establish typical one-hour and eight-hour
values, and also to determine when the peak one-hour and eight-hour rates
will occur, and what their values will be.
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Table 5. SUMMARY OF TRIP GENERATION DATA (ONE-WAY) FOR REGIONAL SHOPPING CENTERS FROM COG REPORT
Range of Trip Peak Hour
Number Range of Generation Volume as
of Shopping Area Rates Parking % of Daily
Investigator Centers (1000's sq.ft.) (per 1000 sq.ft.) Median Spaces Volume
National Cooperative 15 300-800 8-27 1000-5000 11.3-18.3
Highway Research
Program Av_ week day
Los Angeles Regional 5 541-811 9-29 ' 22 3200-6830
Transportation
Study
California Division 1 528 - 39 2500
of Highways
1, 0 St. Louis ITE Study 2 1,000-1,500 26.4-30.6 28 1200-7500
en
Maryland State 6 294-530 22-31 26 1500-3500
Roads Commission
e Bureau of Public 1 503 - 16 2000
Roads Peak hourly
Wilbur Smith and generation, average weekday
Associates 3 275-483 2.80-4.673.46 2000-5000
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In Baker and Funaro's work on parking, we find characteristic data
on seasonal variations, as shown in Table 6, which shows the characteristic
Easter and Christmas peaks, with Christmas the highest.
Table 6. SEASONAL DEMAND FOR SHOPPER PARKING
Month
January
February
March
April
May
June
July
August
September
October
November
December
Percent of Yearly Total
7.5
8.0
9.8
9.0
8.5
9.4
5.4
6.3
7.2
7.5
7.9
13.5
(Easter peak -> secondary maximum)
(Christmas peak - maximum)
The same report presents some characteristic trip generation curves which
show the hourly variation for typical weekdays and Saturdays; examination
of these data in the context of other similar measurements, a,s exemplified
by the surveys shown in Appendix B, shows that they reflect characteristic
and relatively invariant behavior from center to center; the seasonal
data are similarly representative. The weekday hourly peak, occurs during
the period 7 to 9 p.m., with the highest value usually in the 8-9 p.m.
hour. The highest 8-hour weekday period extends from 1 p.m. to 9 P«m.
The highest Saturday hour is found between 1 p.m. and 4 p.m., with the
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highest usually at 3-4 p.m. For the eight hour period, it extends from
10 a.m. to 6 p.m.*
The highest one-hour period on Saturday is about one-and-one-half times
the highest weekday hour. The highest eight-hour period on Saturday is
also about one-and-one-half times the highest eight-hour weekday period.
Thus, we find that Saturday one-hour and eight-hour periods are highest,
and December is the highest month. Hence, the pre-Christmas Saturdays
have both the maximum one-hour and eight-hour values. We are nonetheless
also interested in the weekday evening maximum, for the meteorological
reasons discussed in the section titled Meteorological Aspects, in
considering the traffic conditions associated with the most adverse dis-
persion conditions (at least the most adverse during the center's operat-
ing hours).
The absolute magnitudes and ranges of these values to be used are developed
in the section titled Analysis.
*For each hour, we combined the trips entering and trips leaving, to obtain
the number of cars moving during the hour; it is the maximum and/or topical
values of this figure which we seek. In applications combining this figure
with vehicle running times, we either use one-half the total, with complete
cycle running times, or if there is a significant difference both in the
number of vehicles entering and leaving, and in the entering and leaving
running times, then they may be apportioned appropriately.
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SECTION VI
TRAFFIC PARAMETERS, VALUES AND DERIVATIONS
CONCEPT OF EMISSIONS PER UNIT TIME
In parking areas of shopping centers, maximum vehicle speeds rarely exceed
10 or 15 mph, and average speeds are much lower. The usual procedure for
estimating motor vehicle emissions as a function of vehicle sp^ed is not
very accurate at these low speeds due to:
a. Difficulty in estimating average operating spee4» apd
b. Variation in observed emission rates with slight change in average
operating speed.
For shopping centers, analysis shows that traffic operating and l^heir
related emissions are better considered in units of time (grams/mi flutes)
rather than units of distance (grams/mile), for tjhe following reaspns:
1. The variations in emission per unit time at different; speeds, are,
relatively insignificant at the lowest speeds;* and
2. Traffic movement in the vicinity of a shopping center pan be Described
more accurately and more easily in terms of minutes of running time, than
in terms of average speed, particularly when engine idling can predominate
during congested periods.
Values for automotive pollutant emissions in grams/minute a.t; 1d,le are avail-
able from A Study of Emission from Light Duty Vehicles in Six Cities.**
They are summarized in Table 7. These test data compare well with emission
factors calculated from the current edition of APi-42^** when ponv^rtecj to
grams/minute at various speeds and then extrapolated to zero speed.
*Less than 50 percent increase from idle to 15 mph.
**Reference: Automotive Environmental Systems, Ipc., March 1973. Environ-
mental Protection Agency Publication No. APTD-1497.
***Reference: Compilation of Air Pollutant Emission Factors. April T973.
Environmental Protection Agency Publication Np. AP"4? ($ec,ono| Edition).
/
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Table 7- VEHICLE EXHAUST EMISSIONS AT IDLE IN
GRAMS PER MINUTE*
Pollutant Emissions, gm/min
Carbon monoxide 16.19
Hydrocarbons 1.34
Oxides of Nitrogen 0.11
*These values do not include emissions due to the cold start of engines
or to evaporation of gasoline at the end of a trip ("hot soak"). If
subsequent investigation of the relative magnitude of these emissions,
compared to the totals generated by the methodology of this report,
indicates that they are significant, appropriate values for each cold
start and hot soak can be inserted as the total emissions for the start
and stop modes, respectively. Since data for cold start and hot soak
emissions would be reported per occurrence, there is no need to deter-
mine an associated running time or emission period for the modes.
In applying the recommended procedure of emission estimation, total emis-
sions from the shopping c'enter complex at any time would be the product
of the number of vehicles, times average vehicle running time, times,
the appropriate emission factor from Table 7:
ETotal = (V)-'(RT) (EF), where
V = Traffic volume during period of concern
RT = Average running time, minutes
EF = Emission factor, grams/minute.
Operational Modes in Shopping Centers
For purposes of analysis, traffic movement in the vicinity of a shopping
center has been divided into eight characteristic operational modes.
These are summarized below and shown schematically in Figure 3;
Approach (A) - The time or distance along the immediate access road that
total traffic movement is strongly affected by the vehicles entering and
exiting the shopping center.
-20-
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Access Road
TJ
(O
o
o:
(U
O
o
Store
Area
Peripheral Parking Lot
Figure 3. SCHEMATIC REPRESENTATION OF VEHICLE
OPERATING MODES AT A SHOPPING CENTER
-21-
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Entrance (I) - Movement through the entranqeway, including waiting time
at a traffic control light or in a queue.
Movement in (MI) - Driving time or distance from the entranceway to the
preferred parking spaces, usually the nearest available areas to the store
entrances. Time spent searching for an open space is also included in
this mode. -
Stop (S) - Parking of the vehicle and shutoff of the engine.
Start (St) - Starting of the engine and egress from the parking space.
Movement out (MO) - Driving time or distance from the parking space to
the preferred exitway.
Exit (E) - Movement through the exitway, including waiting time at a traffic
control light or in a queue.
Departure (D) - The time or distance along the immediate access road that
movement continues to be influenced by traffic from the shopping center.
The average running time in each of these modes can be quantified for a
specific shopping center as a function of its physical dimensions, traffic
control devices, and traffic volume.
Base Running Time
There is an average minimum vehicle running time for each shopping center
that is associated with periods of low or zero traffic congestion. This
concept of a minimum or base running time is important because it usually
is the most common (typical) operating condition at the shopping center,
and because at most centers it is expected to be exceeded only during
periods of relatively high traffic volume. The base running time can be
determined from a plan of the shopping center with an additional knowledge
of its traffic control devices and probable driving patterns.
Base running times for the two example shopping centers, Montgomery Mall
and Landmark Center, have been constructed both by time measurement during
simulated driving cycles and by estimates based on dimensions of the
centers, entrance/exit configurations, and expected driving patterns.
Total base running times and average times in each operational mode are
shown in Table 8-
-22-
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Table 8. BASE RUNNING TIMES BY OPERATING MODE AT
TWO SUBURBAN WASHINGTON, D.C. SHOPPING CENTERS
Operational Mode
Approach
Entrance
Movement in
Stop
Start
Movement out
Exit
Departure
Total BRT
Base Running Time, Minutes
Montgomery Mall
0.9
0.1
1.0
0.1
0.1
1.2
0.4
0.9
4.7
Landmark Center
0.5
0.1
0.7
0.1
0.1
1.1
0.5
0.5
3.6
Relationship Between Running Time and Traffic Volume
As traffic volume increases, running times become longer due to congestion.
Some of the constraints to movement that contribute to the longer running
times are:
o Queues at traffic control lights and signs at entrance/exits
e Queues created as vehicles attempt to exit onto uncontrolled access
roads
0 Traffic intersections and merging traffic lanes within the parking
area
o Traffic aisles blocked by vehicles making dropoffs or pickups, or
waiting for parking spaces
a Increased number of pedestrians in parking area.
Generally, total running time is qualitatively related to traffic volume
as shown in Figure 4. The base running time (BRT) can be determined for
a specific shopping center as described above. The magnitude of increase
above the BRT with increased traffic can be estimated from shopping center
and trip generation parameters, by the procedure developed in the section
titled Analysis.
-23-
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BRT
Gate or
Parking Capacity
Traffic Volume
Figure 4. General Relationship Between Traffic Volume and Total Running Time
-------
Identification of Critical Modes for Shopping Centers
Examination of the eight operational modes that were identified indicates
that for shopping centers, running times in some modes are relatively
constant, but that times in others may increase drastically from the base
running time during peak usage and traffic conditions. For shopping
centers, the three modes whose times are most affected by traffic conges-
tion, in order of decreasing impact, are:
1. Exit
2. Movement to a parking space
3. Entrance
Exit and entrance times are functions of the egress and ingress capacities,
respectively, of the individual shopping center entrance/exit ways. As
these capacities are approached or exceeded, running times in the two
modes rapidly increase. Waiting times in the resulting queues become the
primary factors in determining total running times. However, because of
diurnal variations in the number of vehicles entering and leaving shopping
centers, egress and ingress capacities generally are not exceeded simul-
taneously.
Movement time into a parking space, the remaining critical mode, is a
function of the number of free parking spaces. The time in this mode
increases only slightly with shopping center usage until the number of
parked cars approaches the capacity of the lot. As parking capacity is
exceeded, movement time and number of cars moving increases, due to incom-
ing vehicles searching for open spaces or waiting for a space to be vacated.
The parameters developed above a.^e analyzed further with the shopping center
parameters in the Analysis section, and the findings employed in the
Methodology section.
-25-
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SECTION VII
ANALYSIS
In this section we make the necessary interpretation and inferences for
converting the data of the section titled Regional Shopping Parameters into
the relationships needed for input to the methodology of the section
titled Results. In the section titled Traffic Parameters, Values and Deriva-
tions, we identified the entrance/exit capacities, and the parking capacities,
as the center parameters which could, under conditions of exceedance,
increase either, or both, the vehicle running times and the number of
vehicles running.
Typical and Maximum Trip Generation as Functions of Center Size
We need to be able to generate at least some approximate indicator of the
typical and peak trip generation rates expected to be associated with a
given center. While one might simply require the developer to provide
such estimates (and we believe this should be done in any event), we
felt it appropriate to examine data on such rates to ascertain any depen-
dencies that might be unearthed, as well as to demonstrate characteristic
values. The regional center data in the COG report turned out to be the
most complete sample which was readily accessible, and the following
statements and interpretations derive from that study. The basic trip
generation parameter used is that of trips generated per 1000 square feet
of shopping space, as the most generalized and customary form of use.
We sought dependencies, and were able to find none, in essence. For any
dependence on parking capacity, data were sparse, and did not indicate
any dependency; none was expected, in any event, with the possible excep-
tion that centers with very low parking capacities would dissuade potential
-26-
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customers, and thus result in low trip generation. This situation would
generally not be of interest.
The data on trip generation were stratified by center size, with the
result seen the left-hand portion of in Table 9. Examining these data
in a number of different ways indicates a relative independence of the
trip generation rate from the center size (means and median nearly con-
stant from 300,000 to 900,000 square feet in center size; above that, an
apparent slight decrease). Of course, the absolute total number of trips
generated is directly dependent on center size; that parameter has been
factored out in seeking more precise dependencies. Some relationships
appear for the rates, however, which we will accept in spite of the
smaller number of cases at the larger sizes, because the relationships also
appear to fit expectation. For example, the highest values seen in the
"range" column tend to diminish at the larger center sizes; this would
agree with the concept that there is a demographically-inposed limit on the
absolute maximum value of trip generation which is possible. In other
words, one cannot attract ever larger numbers of people simply by increas-
ing shopping space ad infinitum. On the other hand, it is probable, for
parallel but converse reasons, that the minimum values for the largest
centers may be larger than those for smaller centers; this is only of
incidental importance, however.
The ranges of values seen for each center size range are undoubtedly due
to combinations of factors, including real demographic and shopping center
differences, as well as the probabilities of sampling, and real differences
in measurement procedures. We must accordingly interpret these as "expected"
ranges, to be used for sensitivity study purposes, as first approximation
for typical values for a center with no data, and as a comparison against
which to assess the reasonableness of submitted estimates. The values
proposed in the right-hand protion of the table (headed "Suggested for
Use") are proposed for use where necessary in the succeeding methodology.
-27-
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Table 9. TRIP GENERATION DATA FROM THE COG REPORT, AND SUGGESTED VALUES
FOR USE, AS A FUNCTION OF REGIONAL SHOPPING CENTER SIZE
Center Size
1000's sq.ft.
300-399
400-499
400-599
700-900
1,000-1,500
Total
Number
of
Centers
11
3
10
4
2
30
"Average Day" One-Way Trip Generation
Rates (per 1000 sq.ft. per day)
From the COG Report
Median
20
15
20
18
14
Mean
20
17
19
18
14
Range
8-31
14-21
9-29
9-26
13-15
Suggested for Use
Median
20
20
20
18
15
Mean
20
19
19
18
15
Range
10-30
10-30
10-30 '
10-25
10-15
Certain additional numbers are required for the methodology.
These are obtained from, and are here added to, the material which con-
cludes the section titled Regional Shopping Center Parameters. Table 9
above provides a selection of "average day" rates for any center
of interest. Multiplied by the thousands of square feet in the center
gives the typical total one-way trips generated as our base figure. Data
from the section titled Regional Shopping Center Parameters, augmented here,
gives us the information that, of that total_,_the weekday peak JTpjjr__j_s_
J1 percent and the weekday peak eight-hour period is 70 percent. The
Saturday total is 1.15 times the weekday, and, of the Saturday total,
its peak hour is 15 percent and its peak eight-hour is 80 percent.
Recalling that all these are typical values, from the seasonal variability
data of the section titled Regional Shopping Center Parameters we see that,
for the seasonal December peak, we would multiply all values by a factor
o£J.4. Thus, we can evolve all the necessary trip generation data from
this section.
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GATE CAPACITY EXCEEDANCE AS A FUNCTION OF TRIP GENERATION AND GATE
CA-PACITY - RESULTING RUNNING TIME INCREASE (E)
Average running times for entrance and exit are primarily functions of
three parameters: traffic trips in and out of the center, entrance and
exit capacities, and the time sequences of the traffic control devices
at the entrance/exit (gates). Running time can be quantified with data
on these three parameters for a shopping center, by use of a methodology
en.ploying queueing theory.
The entrance and exit capacities for a shopping center are each considered
to be constant over the time frame (one-hour) of this analysis, although
they actually do vary with the traffic volumes on arterials adjacent to the
center. The estimated gate capacities should be submitted by the developer,
but they may also be approximated from such information as the number of
gates, lanes at each gate, time sequences on traffic control lights at the
gates, and respective percentages of traffic making left and right turns.
The total traffic entering or leaving the center during any incremental
period (trip generation) can be determined form the data on daily and sea-
sonal variations that were previously presented in the section titled
Regional Shopping Center Parameters, and from the section titled Analysis.
These data should, if possible, be calibrated to match the expected traffic
for each specific shopping center; and may need to be further adjusted to
account for atypical variations at the center, either anticipated or
observed.
This procedure, utilizing total gate capacities and trip generation data
for the center, assumes that the shoppers will distribute themselves among
the available gates so as to minimize their running times. While this
assumption has some validity based on observed behavior, it may be desirable
to perform the analysis on an individual gate basis if significant devia-
tions in waiting time are known (or suspected) to exist, and if data on
traffic volumes at each gate can be obtained. Unbalanced use of gates is
most likely to occur where a single arterial or freeway carries most of
the traffic to and from the center.
-29-
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Estimates of running times for the entrance and exit modes cannot be
precise, especially considering the available input data. The equations
employed here for waiting time in queue result from assumptions that
vehicles are reaching the gate randomly over the time increment of concern,
and are passing through the gate randomly; hence, their distribution con-
forms to the negative exponential law, with the queue discipline the first-
come-first-served rule (classic basic queueing theory). Errors in the
estimates by use of these equations are thought to be relatively low.
For periods when traffic flow is less than gate capacity, the average
running time (in minutes) in a queue is given by the equation:
RT = b (y^-J + c, where
a = utilization factor
traffic flow, veh/um't time
gate capacity, veh/unit time
b = average outflow time per vehicle (inverse of gate capacity),
min.
c = added running time at a discontinuous entrance/exit (traffic
control signal), min.
= 0.5 (fraction of signal cycle on red) (length of red light,
min.)
An example calculation when the traffic flow does not exceed capacity is
as follows. If the traffic flow through all entrances during a one-hour
period is 1400 vehicles, the combined entrance capacity is 2000 vehicles
per hour, and entrances to the center are controlled by lights on 1.5
minute cycles with 50 percent red, the average running time for the
entrance mode would be:
= 0.445 .minutes.
-------
For these periods when traffic flow exceeds gate capacity, the queue
continues to build during each time increment by the amount that traffic
volume exceeds capacity. Average running time for this situation can
best be estimated by the tabular calculation procedure exemplified
in Table 10. The procedure is illustrated with data for a two-hour peak
traffic period (3:00p.m. - 5:00p.m.) with vehicles existing as shown in
column 2 and an exit capacity of 1200 vehicles per half hour.
-31-
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Table 10. EXAMPLE QUEUE CALCULATION WHEN GATE CAPACITY IS EXCEEDED
I
CO
ro
4
1
Time Period
Starting Ending
2:30 3:00
3:00 3:30
3:30 4:00
4:00 4:30
4:30 5:00
5:00 5:30
5:30 6:00
6:00 6:30
2
Traffic
Vol ume
(in or out)
900
1220
1400
1600
',400
1100
980
750
3
Gate
Capacity
(in or out)
1200
1200
1200
1200
1200
1200
1200
1200
4
AN
col . 3-
col. 2
-
+ 20
+ 200
+ 400
+ 200
- 100
-220
-440
5
N at End
of Period
col. 4+
col. 5'
(line above)
-
20
220
620
820
720
500
60
6
NAV.
col. 5+col . 5'
2
-
20
120
420
720
770
610
280
7
RT
(b) (col. 6)
(use equation)
.25
3.0
10.5
18.0
19.25
15.25
7.0
N = queue length, in cars
RT = average running time, in minutes
= (av. outflow time per vehicle, min.) (av. queue length)
-------
PARKING CAPACITY EXCEEDANCE AS A FUNCTION OF TRIP GENERATION, CENTER SIZE,
AND PARKING CAPACITY - RESULTING RUNNING TIME INCREASE
All of the information analyzed, interpreted and used in this section comes
from the Urban Land Institute (ULI) survey of parking requirements for shop-
ping centers. Their data were analyzed and interpreted by them as support-
ing the contention that "in operational practice and hence for development
planning purposes, where there is virtually no walk-in trade nor public
transit usage, the provision of 5.5 car parking spaces per thousand square
feet of gross leasable area is adequate as a standard to meet the demand for
parking space at shopping centers. This standard accommodates the need for
parking spaces for all but the 10 highest hours of demand during the entire
year. These 10 highestjKUjrs occur during the three peak days of the year.
It is uneconomic to provide parking space for such limited peak demands."
The data they generated to study the occasions of exceedance of parking
lot capacity is of use to us here in determining the extent of, and reasons
for, this exceedance.
It is of utmost importance as a preliminary, to note that some center managers
who have problems of parking exceedance on extreme occasions, will take
special steps to alleviate the situation, such as leasing adjacent parking
space and lots for the critical period. If such exceedance is anticipated,
and such remedial steps are planned, than the following phase of the analysis
becomes irrelevant and should be bypassed. Developers should be queried on
this point, to ascertain whether they anticipate, and will arrange for, this
problem.
Continuing, as an adjunct to the ULI survey on parking capacity exceedance,
we may add the qualitative observations made by some of the Greater Washington
center managers, which ranged from "the parking lot has never been full,"
through "the lot is full on Saturdays before Christmas," to "the lot is full
every Saturday, on sale days, and from Thanksgiving to Christmas." These
comments generally fit qualitatively with the corresponding parking space
rate data, (in spaces per thousand square feet of shopping space), in the
sense that higher rates cause fewer full lot cases, and vice versa. The
problem is complicated, however, by the trip generation phenomenon.
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Continuing further with the analysis: ULI confirmed the pre-Christmas
period as that of the peak demand, and focussed their survey study of
270 centers on that peak. Ninety-three of the 270 centers were 300,000
square feet in shopping space, and hence qualified as regional centers and
were used in our analysis.
We will focus, exclusively on the peak hour rather than the peak eight-
hour period, since our interpretation of the ULI data indicates that such
one-hour peaks appear to be of short duration, and thus would not seriously
affect the peak eight-hour period. Since the one-hour peak occurs within
the peak eight-hour period, its increased number of vehicles and running
times may be used to augment the eight-hour value, if the one-hour figures
are significant compared to the eight-hour.
The ULI data on peak one-hour parking capacity exceedance have been analyzed
with the results given below. Remember that these data are measured for the
peak hour, and they thus only apply to the peak hours derived as described
in the section titled Typical and Maximum Trip Generation as Functions of
Center Size. We find that of the 93 peak hours for regional centers,
22, or about 25 percent, reached or exceeded the parking lot capacity.
This distillation by us of a rather extensive and varied body of data
indicates that, on the average, in one case in four the parking lot capacity
will be exceeded during the peak hour. This summary finding is elaborated
below.
It is logical to assume that the 25 percent parking exceedance cases will
occur for those centers with the lower parking rates and the higher range
of trip generation values. Thus we found, for all these exceedance cases,
that there is also a dependence, as might be expected, on parking space
rate per 1000 square feet of shopping space. This is demonstrated by the
following: for centers with parking space rates less than four spaces per
thousand square feet of shopping space, the parking capacity is exceeded
two times out of three (66 percent of the time) at times of peak use; at
parking rates of four to less than six, the exceedances occur 30 percent of
the time; at rates of 6 to 8, 20 percent; and above 8, essentially not at
all. As expected, the exceedance decreases as we provide (relatively) more
parking spaces. /
-34-
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We interpret this quantitatively as follows: when working with peak
Christmas hours, if the parking space rate is less than 4, and the trip
generation rate is in the higher two-thirds of the possible range, then
parking exceedance must be accounted for, as discussed below. Similarly,.
the parking exceedance must be analyzed, when parking rates are 4 to 6
and the trip rate is in the highest 30 percent; or with parking rates of
6 to 8 and the high 20 percent of trip generation. Above parking rates
of 8, parking exceedance is not expected to be a problem.
The above accounts for the circumstances when parking exceedance will be
a problem, on the average. As to the amounts of the exceedances, the number
of ext,ra__cars which will be running during the peak hour of concern, also
derived from the ULI data, will be as follows, as percentages of the parking
lot capacity: 20 percent of the parking lot capacity will be the number of
cars running for the <4 parking rate case; for the 4-6 case, 10 percent;
and for the 6-8 case, 5 percent. Each of these percentages represent a
number of additional vehicles (as a fraction of the number of parking spaces)
which will be running during the peak hour, because of the exceedance.
We thus have a process of elimination which determines the cases of peak
hours for which parking exceedance must be accounted for and how to account
for them. First, if the parking rate is eight spaces per thousand square
feet of shopping space, or greater, parking exceedance is almost certainly
not a problem. For.parking rates of 6 up to 8, and peak hourly trip
generation rates in the high 20 percent, parking exceedance will consist
of 5 percent of the parking lot capacity, in numbers of cars running during
the peak hour. For parking rates of 4 up to 6, and peak trip rates in the
high 30 percent, parking exceedance will consist of 10 percent of the lot
capacity, and rates less than 4, and peak trip rates in the high 66 percent,
the corresponding parking exceedance figure will be 20 percent.
These numbers become input to the analytical methodology described in the
section titled The Methodology.
-35-
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SECTION VIII
RESULTS
THE METHODOLOGY
In general terms, the methodology proceeds as described in the first para-
graph which follows. We wish to emphasize that this description is of the
technique, shown schematically in Figure 5 , in its most general form, and
as such will provide the starting for each of the complexes to be studied
in subsequent tasks. Differences in implementation are expected to arise
in the case of each complex.
Starting from the physical, geographic, and demographic characteristics of
the complex, we use the concepts of operational traffic modes to generate
best estimates of typical and peak trip generation rates, and of base
running times for cars associated with the center. We also define the
parameters of the center which significantly and adversely impact traffic
behavior. The typical trip rates and base running times provide the data
for typ.ical conditions for the required time periods. Quantitative rela-
tionships are defined or estimated for the controlling center parameters
and affected traffic modes, and these in turn are superimposed on the base
running times to generate peak running times. The peak running times are
then associated with peak trip generation rates to create the peak infor-
mation for the required time periods. We next see how this generality
becomes more specific for a given type of complex.
In the case of shopping centers, as shown in Figure 6, the methodology
proceeds from basic information about a given shopping center (see the
section titled "Regional Shopping Center Parameters"), via traffic
behavior data (see the section titled "Traffic Parameters, Values and
-36-
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Characteristic
Parameters
of Complex
Seak
rip
ration
lues
C!xceedance\
Values I
/"TyplcalN
( Trip )
^GenerationJ
N^ Valuga-X
Peak \
Running j
Timeslx
Peak Values
of Numbers of
^Cars Running, am
vBase Running
Timp
: Basic
Running
Time
Peak ValuesX
of Numbers of \
"iCars Running, and]
Peak Running J
Times ^/
typical Values
of Numbers of
\Cars Running, ani
Base Running
Times
Figure 5. Generalized Methodology
-37-
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Sec. V
C Parameters j
of J
>pping Center/
V v
V ,-
'Number of
Gates and
Gate
^Capacity^
/ParkingX
(Spaces per ]
\1000 sq.ft/
V ,,
, S1-ze-
[1000's sq.
:t. Shippim
Schematic
Layout
GPeak TripN
veneration
Values
Gate
lExceedance
S
f Parking
\Exceedance
VIIv
/Excess Time
(and Numbers
\pf Cars
Excess Tim
nd Numbers
of Cars
Cars Running, andM
VII
/"Typical^
[Trip Gener- )
V ation /
v VIII
Peak Values
of Numbers of
[Cars Running, and]
Base Running
Times
VI
/^Base
I Running
\Time
'Typical Values^
of Numbers of
irs Running, andj
Base Running
Times
Figure 6. Generalized Methodology Applied to
Shopping Centers
-38-
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Derivations"), and typical trip generation data (see the section titled
"Typical and Maximum Trip Generation as Functions of Center Size"), to
generate estimates of typical numbers of vehicles and associated running
times for one-hour and eight-hour periods; these are two of the required
end products of the task. For the maximum case, maximum trip generation
rates are estimated (see the section titled "Typical and Maximum Trip
Generation as Functions of Center Size") and then used to obtain exceedance
estimates for both gate capacity and parking capacity, the two principal
controlling parameters (see the sections titled "Gate Capacity Exceedance
as Functions of Trip Generation and Gate Capacity - Resulting Running Time
Increase (E)" and "Parking Capacity Exceedance as Functions of Trip Gener-
ation, Center Size and Parking Capacity - Resulting Running Time Increase
(MI)"); these latter generate the associated increases in both numbers of
vehicles running, and vehicle running times. These increases are combined
with the base numbers described above, to provide the other two major
products, the peak running times and vehicle numbers for one-hour and
eight-hour periods.
The specifics of the procedure, with examples, are presented in the fol-
lowing paragraphs. It is easiest done with the occasional use of examples,
but the general applicability will be evident.
First, we define our existing or proposed center by means of a schematic
diagram, and available or estimated data on: the area covered by commercial
enterprise, the number of parking spaces, and the number of entrances and
exits and their capacity; also required are best estimates of typical and
maximum trip generation rates. If any of these parameter values are
uncertain, then the estimated range should be provided, and the analysis
carried out as a sensitivity study in order to determine the importance
of the parameter value.
The schematic enables estimates to be made of the base running time,.
according to the procedures set forth in the section titled "Traffic
Parameters, Values and Derivations." Estimates are made as shown in
Figure 3 and Table 8 of that section; the example values given there for
-39-
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Greater Washington's Montgomery Mall and Landmark Center are 4.7 and 3.6
minutes, respectively.
If typical and peak trip generation data are not available, then Table 9
of the section titled "Typical and Maximum Trip Generation as Functions of
Center Size" may be used to estimate the probable range of typical and
peak trip generation rates which may be encountered for a center of the
selected size (thousands of square feet) of commercial floor space. Some
indication must be provided, however, of approximately where, in the prob-
able ranges, the center's values will fall.
The trip generation data may be in the form of trips into, or out of, the
center, or both. The traffic mode cycle includes both, so that any equiv-
alent value of trips into or out of the center will suffice. Thus, as a
good first approximation, at least for shopping centers, we can safely
assume that any trip in, in a given typical or peak hour, is accompanied
by a trip out, so that the number of vehicles running is given by the one-
way trip generation rate; the accompanying running time is usually the
average base running time for a complete cycle (see the section titled
"Traffic Parameters, Values and Derivations"). However, typical trip
generation for the center must be checked against the section titled
"Gate Capacity Exceedance as Functions of Trip Generation and Gate Capa-
city - Resulting Running Time Increase (E)" to ensure that there are not
gate queues under typical conditions. If so, see the next paragraph.
We now proceed, for the peak case, to the methods of the sections titled
"Gate Capacity Exceedance as Functions of Trip Generation and Gate Capa-
city - Resulting Running Time Increase (E)" and "Parking Capacity Exceedance
as Functions of Trip Generation, Center Size and Parking Capacity - Resulting
Running Time Increase (MI)" to estimate the effects of the given peak trip
generation rate on vehicle running time, resulting from any exceedances of
gate or parking capacity which may result. Using the equation and the
hypothetical example tables of the section titled "Gate Capacity Exceedance
as Functions of Trip Generation and Gate Capacity - Resulting Running Time
Increase (E)," we find that at a given peak trip generation rate, the "extt"
-40-
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running times are increased, because of gate capacity exceedance, from a
value of 0.15 minutes for the non-peak case (from the equation, with a
900 vehicle 1/2-hour traffic volume entering a gate with 1200 vehicle
1/2-hour capacity) to almost 20 minutes, in the last half-hour of the
peak two-hour demand (as shown in the table).
The "movement in" running times might also be increased, as exemplified by
the hypothetical calculation in the section titled "Parking Capacity
Exceedance as Functions of Trip Generation, Center Size and Parking
Capacity - Resulting Running Time Increase (MI)."
The resulting increases in times are added to the base running time to
give the peak running time; the peak trip generation rate will, as for
the typical case, give the base peak number of vehicles running, to which
we add any additional vehicles running because of gate and/or parking exceedance.
We thus have the four basic numbers required for each of the two time periods
for input to the emission rate calculations: typical and peak numbers of
vehicles running during one-hour and eight-hour periods, and their associated
typical and peak values of vehicle running times.
GEOGRAPHIC DISTRIBUTION
Running times, and hence emissions, from a shopping center complex can
usually be considered as being distributed fairly uniformly over the area
of the center during typical operating periods (base running times), as
indicated by the schematic in Figure 3 and the example data in Table 8
(see the section titled "Traffic Parameters, Values and Derivations").
For most analyses, an assumption of a geographically uniform emission
density is thus sufficiently accurate.
Peak traffic conditions can result in either the gate or the parking capa-
cities being exceeded, or both. If only the parking capacity is exceeded,
emissions still tend to be distributed evenly over the entire parking area,
as drivers search for empty parking spaces. However, if gate capacity
is exceeded, a substantial part of the total running time and emissions
become concentrated at the entrance/exit ways.
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The procedure of estimating running time for each mode individually allows
this uneven distribution to be evaluated quantitatively. Emissions from
the ensuing traffic queue can be simulated as a continuously emitting line
souree(s) oriented from the gate along the main queue line, while emis-
sions from the other seven modes are still considered to be uniformly
distributed over the shopping center area, as above.
METEOROLOGICAL ASPECTS*
The meteorological characteristics which most importantly affect atmospheric
dilutive capacity are mixing height, wind speed and atmospheric stability.
A convenient summary of mixing height and wind speed characteristics which
affect air pollution potential is given in the Office of Air Programs
Publication No. AP-101 (Holzworth 1972). Atmospheric stability may be
determined in terms of cloud cover, solar radiation and wind speed by a
method proposed by Pasquill and shown in Table 10 . For ground level
sources, such as automobiles in shopping centers, the ground level con-
centrations, both in the vicinity and downwind of the sources will be
inversely proportional to wind speed and mixing height and directly pro-
portional to atmospheric stability (i.e., the more stable the atmosphere,
the higher the concentration).
The season of peak use of shopping centers is cited as the 12 shopping
days preceding Christmas in the section titled "Regional Shopping Center
Parameters," with the highest day usually being the Saturday before
Christmas. The peak hour of use on any given Saturday is 'generally
3 to 4p.m. The peak eight-hour period is generally 10 a.m. to 6 p.m.
A secondary one-hour peak use period occurs during weekday evening hours
at various times between 7 and 9 p.m. This peak averages about two-
thirds of the Saturday afternoon peak.
Since the pre-Christmas period occurs during the transition from autumn
to winter, the meteorological conditions which characterize the period
of peak use of shopping centers should be estimated by interpolating
*This section was prepared by Mr. Robert C. Koch, Senior Research Scientist
of GEOMET, Incorporated.
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Table n. KEY TO STABILITY CATEGORIES (after Turner 1970)
Day
Night
Surface Win
Speed (at 10
m sec"1
<2
2-3
3-5
5-6
>6
d Incoming Solar Radiation
m\
m; ,
Strong
A
A-B
B
C
C
Moderate
A-B
B
B-C
C-D
C
Slight
B
C
C
D
D
Thinly Overcast
or
>_ 4/8 Low Cloud
E
D
D
D
<3/8
Cloud
F
E
D
D
The neutral class, D, should be assumed for overcast conditions during day
or night.
NOTE: Class A is the most unstable, class F the most stable class. Night
refers to the period from 1 hour before sunset to 1 hour after sunrise.
Note that the neutral class, D, can be assumed for overcast conditions
during day or night, regardless of wind speed.
"Strong" incoming solar radiation corresponds to a solar altitude greater
than 60° with clear skies; "slight" insolation corresponds to a solar
altitude from 15° to 35° with clear skies. Table 170, Solar Altitude and
Azimuth, in the Smithsonian Meteorological Tables (List 1951) can be used
in determining the solar altitude. Cloudiness will decrease incoming
solar radiation and should be considered along with solar altitude in
determining solar radiation. Incoming radiation that would be strong
with clear skies can be expected to be reduced to moderate with broken
(5/8 to 7/8 cloud cover) middle clouds and to slight with broken low
clouds.
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-------
Figure 7. Isopleths (m sec"^) of mean autumn wind speed averaged through the afternoon mixing layer
-------
Figure 8. Isopleths (m sec"1) of mean winter wind speed averaged through the afternoon mixing layer
-------
10
12
14
16
Figure 9. Isopleths (m x 102) of mean autumn afternoon mixing heights
-------
10
12
Figure lo.lsopleths (m x 10^) of mean winter
mixing heights
-------
QUALITATIVE GUIDELINES
In addition to the quantitative evaluation procedures developed above, the
review of shopping centers as complex emission sources should also include
the following considerations which are not presently reducible to quantita-
tive terms:
1. Main entrance/exitways should preferably be on a highly visible local
secondary street that feeds into the nearest arterial, so that the transi-
tion from highway driving to parking lot driving and vice versa are not too
abrupt.
2. Any left turn movement across traffic flow that is used by a signif-
icant number of the shopping center patrons is a potentially large conges-
tion point and emission problem.
3. At centers with multiple entrance/exits, the vehicles will generally
tend to distribute themselves among the available gates to minimize their
running times. However, if unbalanced gate use does occur, it may be
reduced by stationing personnel in the parking area to divert outgoing
traffic from the overburdened exit or by the use of traffic information
signs.
4. Personnel (including police) may also be effectively used to speed
traffic flow during periods of highest congestion.
5. Prior provision for overflow parking in a temporary or remote lot
may be an appropriate requirement for centers with anticipated marginal
parking capacities.
6. The design of curbed entrances and exit "streets" within the parking
lot (but separate from the parking areas) may reduce the interference of
exit queues with in-lot movemer. - and move congestion points away from the
gates to a more desirable (or controllable) location in the lot.
THE NINE QUESTIONS
While the specific information called for by the task work statement
has been provided in the sections from Regional Shopping Center Parameters
through Meteorological Aspects, the nine questions spelled out as part of
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the statement warrant specific response. This is given here, with the
question abbreviated.
1. Area allotted to or occupied by a single vehicle? The area ranges
from 9 x 20 feet (180 ft2) to 10 x 20 feet (200 ft2).
2. Percentage of land and parking spaces potentially occupied by
vehicles? The usual percentage? These data are given in section titled
Regional Shopping Center Parameters and used in the sections titled
Analysis through The Methodology. ;
3. Typical and peak values (absolute or fractional) of vehicles runn-
ing for one- and eight-hour periods? These data are developed in sections
Analysis through The Methodology.
4. Typical and worst case (slowest) vehicle speeds? In the context of
our approach, this question is only relevant to analysis of the "Major
Highway" complex source task. It will be dealt with in that task report.
5. Vehicle distribution within the complex? See section titled Geo-
Graphic Distribution.
6. Design parameters of the complex likely to be known beforehand?
See section titled Regional Shopping Center Parameters.
7. Design parameters in question (6) which can be most successfully
related to traffic, and hence emissions? See section titled Analysis.
8. Relationships of parking lot design to parking densities and vehicle
circulation? What is typical design? Design with highest parking densities,
lowest vehicle speeds, longest vehicle operating times? To the extent to
which these questions are relevant to our methodology, they are answered
in the section titled Regional Shopping Center Parameters through sections
titled Traffic Parameters, Values and Derivation and Analysis.
9. Meteorological conditions likely to occur during peak use? Use
level during periods of worst meteorology? See section titled Meteprological
Aspects.
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SECTION IX
DATA SOURCES
BOOKS AND REPORTS
Baker, Geoffrey, and Bruno Funaro. Shopping Centers - Design and Operation.
Reinhold Publishing Corporation, Progressive Architecture Library.
New York City.
Baker, Geoffrey, and Bruno Funaro. Parking. Reinhold Publishing Corporation,
Progressive Architecture Library. New York City.
Lynch, Kevin. Site Planning. The M.I.T. Press, Massachusetts Institute
of Technology, Cambridge, Massachusetts.
Gruen, Victor, and Larry Smith. Shopping Towns U.S.A - the Planning of
Shopping Centers. Reinhold Publishing Corporation, Progressive Architecture
Library. New York City.
Metropolitan Washington Council of Governments, National Capital Region
Transportation Planning Board. Traffic Characteristics of Shopping
Centers - A Review of Existing Data. Technical Report No. 3, July 1970.
Urban Land Institute. Planning Requirements for Shopping Centers - A
Survey. Technical Bulletin 53. Research sponsored by the Research
Foundation of the International Council of Shopping Centers.
Automotive Environmental Systems, Inc. A Study of Emissions from Light
Duty Vehicles in Six Cities. EPA Document No. APTD-1497. March 1973.
Maryland Bureau of Air Quality Control. Method for Estimating Light Duty
Vehicle Emission on a Sub-Regional Basis. Report BAQC-TM 73-107.
April 1973.
U.S. Environmental Protection Agency. Compilation of Air Pollutant Emission
Factors (Second Edition). EPA Publication No. AP-42, April 1973.
Architectural Record. Building Types Study 432: Shopping Malls in Suburbia.
Vol. 151, No. 3. March 1972.
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Zoning Ordinances for Various Communities.
Washingtonian Magazine. Supermalls! August 1973, pp. 58-63.
PRIVATE COMMUNICATIONS
Information provided by the General Managers of the following Greater
Washington Shopping centers: Tysons Corner, Landover Mall, Wheaton Plaza,
Prince Georges Plaza, Beltway Plaza, Landmark Center, Iverson Mall, Seven
Corners Center, and Rockville Mall.
Information provided by Felipe LeBron and Alvin Bowles, Maryland Bureau
of Air Quality Control.
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APPENDIX A
Illustrative Notices of Construction and Modification for
Complex Sources - Data Required from Developer to
Enable the Determination of Air Quality Impact to be Made
The following information is typical of that needed for an air
qonlit}' impact determination of direct on-site stationary source activ-
ities and indirect mobile source activities. The information listed
within is merely typical and can be tailored in kind, and format, to
suit specific agency requirements.
It is .suggested that the requirement for filing of Parts I-III
(bnoic data submission) be made mandatory and for Part IV (air quality
impact evaluation) be made at the discretion of the developer. The
incentive to the developer for filing Part IV would be based on the
developer's desire to be acquainted with the technical requirements of
complex source design and the shorter tiir.n period required for revisv/
if Part IV were to be voluntarily filed.
Operating information required from developers should be
b;. ~.ed on anticipated normal operations, particularly where maximum
or worst case conditions are to be calculated by the agency, i, c. , the
worst case to be calculated should be that worst for a condition of nor-
mr.l di'ily operations. It is misleading to submit data for the absolutely
worwt c;!.sc th;it could £v*!r occur shirr: this would be a complex situation
ihut in any event, v/ould ho covered by emergency ^ctiony on the part of
nil [-Dilution <-on!rol nj;cncio.s.
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APPENDIX
NOTICE FOR CONSTRUCTION OR MODIFICATION OF COMPLEX SOURCE
PART I General T.nformat:ion
1. Owner of Installation
2. Mailing Address
3. Date of Application
4. Telephone Number
5. Applicant or Authorized Agent
6. Telephone of Authorized Agent
i .'
7. Mailing Address
'/
8. Street Address of Complex Source.
9. City, Town or P.O. and County of Cpuiplex; Source
10. Installer or .Contractor (if'new or replacement)
11. Telephone of Installer or Contractor
12. Mailing Address
13. Starting Date of Construction
14. Completion Date of Construction
15. Date Existing Ins"allation Placed in Qperatiqn
16. Signature of Ov?ncr or Authorized Company Official
17. Title of Owner or Authorised Company Official
18. Type of Registration
(1) lix.tr-ting Installation (Initial KcgU-lrati
(2) .Hew Installation (To.be constructed)
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(3) Replacement
(a) Alteration
(b) Addition
(c) Change of Ownership
(4) Other (Specify)
19. Operating Procedures
(1) Usual number of hours per day this installation
will operate
(2) Busiest Day(s) of Year Normally Anticipated
(3) Busiest Hour(s) of Day Normally Anticipated"
PART II Mobile Source Information
(1) Parking Facility
(a) Normal Capacity for Motor Vehicles
(2) Niimber f Vehicles Entering the Parking Facility
per Time Period during Normal Operation
(a) Vehicles per peak hour of operation
(b) Vehicles per busiest day of the year
'_v. »**-
-------
1.2 Name of Fuel Supplier
(a) Address of Fuel Supplier
(b) 'Telephone of Fuel Supplier
1.3 Stack Height above Ground Level (in feet)
1.4 Type of Fuel used
1.5 Type, of Burner used
1.6 Amount of Fuel Consumed Annually
1.7 Sulfur Content of Fuel to the Nearest
tenth of one percent
1.8 Maximum Firing rate (BTU 'per hour input)
B. Incineration
1.0 If On-Site Incineration is to be used,
complete the following
1.1 Name of Manufacturer
1.2 Rated Capacity
1.3 Type of Air Pollution control device
Part IV Impact of Complex Source on Air QuftlJty - to
be filed at discretion of developer
1. On-Site AJr Quality
(a) Calculation maximum on-site carbon monoxide.
Concentration:_ ppm per hour(s).
2 Traffic Data and Off-Site Air Quality
Describe, the con.dj.ti.on of traffic within a one-half
mile radius of the proposed complex source for the current
your, and projected for the complex sources first; and fifth
full years of operation. In thiy discussion include the
following avid Illustrate-, how all data was obtained or
calculated.
t
(a) A current and projected nap of local sfrcqts,
exprcsuwayr, and free-ways within a pnc-rhnjf
nu'J e radiu:; oi: the proposed site,.
-56.-
-------
(b) Current ambient carbon monoxide concentrations
along the roadways cited in (a).(Include only
if available from local air pollution control
agency).
(c) Current and projected vehicular flow (per hour,
day, and year) along the roadways cited in (a).
(d) Current and projected vehicular carbon-monoxide
emissions along the roadx^ays cited in (a).
(e) Projected ambient carbon monoxide concentrations
along the roadways cited in (a). (Include only
if available from local air pollution control
agency.)
-57-
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TRAFFIC CHARACTERISTICS
FOR
MONTGOMERY MALL
June 1970
-------
APPENDIX B
TRAFFIC SUREYS FOR MONTGOMERY MALL AND LANDMARK CENTER
-------
%0-15, T978
June 24-30, 1970
TABLE Bl. TOTAL TRAFFIC ENTERING AND LEAVING THE
SHOPPING CENTER DURING AVERAGE WEEKDAY AND
SATURDAY.
Average Weekday: 26,500
Saturday : 31,800
TABLE B2. RELATIVE USE OF ACCESS POINTS AT THE SHOPPING
CENTER FOR AN AVERAGE WEEKDAY AND SATURDAY.
Ent./Exit Nu
1
2
3
4
5
Total
mh^r* -Avg.
In
66%
6%
14%
2%
12%
100%
. Weekday
Out
64%
5%
15%
2%
14%
100%
Saturday
In
65%
6%
14%
1%
14%
100%
Out
63%
5%
15%
1%
16%
100%
*See Figure AT for location of Entrances/Exits.
-58-
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Figure B2.
MS
^JWEEKWjl^
6 EiIHAEEielENIM INfr ANI>;
!:';':: T~; '~^£.~f\'.'.l-- :i~:;7:::ili:;r:;i;;:;p.;'!:~:;' -"':'::;"
-------
FIGURE Bl.
i
cr>
o
-------
TRAFFIC CHARACTERISTICS
FOR
LANDMARK SHOPPING CENTER
May 1970
-------
I. TRAFFIC DATA COLLECTION
The results presented in this report are based on traffic counts taken
at the shopping center from May 22-28, 1970.
II. TRAFFIC DATA ANALYSIS
Table B-3 lists the total vehicles counted entering and leaving the shopping
center during the average weekday and Saturday.
Table B-2 shows the relative use of entrances and exits at the shopping
center during an average weekday and a Saturday. Figure 1 shows the
locations of the entrances and exits.
Figures 3 and 4 show the hourly volumes expressed as a percent of the
average hour in and out-of the shopping center for the average weekday
and for Saturday.
-61-
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TABLE B3 TOTAL TRAFFIC ENTERING AND LEAVING THE
SHOPPING CENTER DURING .AVERAGE WEEKDAY AND
SATURDAY.
Average Weekday: 29,500
Saturday , : 33,000
TABLE B4. RELATIVE USE OF ACCESS POINTS AT THE SHOPPING
CENTER FOR AN AVERAGE WEEKDAY AND SATURDAY.
Ent./Exit Number* Avg.
1
2
3
4
5
Total
In
53%
13
2
32
100%
Weekday
Out
22
42
36
100%
Saturday
In
51%
14
2
33
100%
Out
26%
31
43
100%
* See Figure 2 for location of Entrances/Exits
-62-
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ZF&
i i
ZOO
Figure 5-4.
HOURLY
T
'J^. :. : | ::..-..! .
^Z ...J-...-.-4 . .:.' .'._..-.. .
: titf -i >*>>! . !
iAtf ' j
5*0
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AH 5L;,^,.:..* §;:-.-»,
68, J0 I2L
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- ':
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-
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-
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'~
. .
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~~^r i
"I -;.' !
^ ; _]
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* i
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:::-|;:v: .;.
;H::;-h.i!i;|r-.i|::-.!l!|lii
-------
HOURLY VARIftTf aM
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-45073-74-003-a
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
5. REPORT DATE
Vehicle Behavior In and Around Complex Sources and
Related Complex Source Characteristics
Volume I - Shopping Centers
August 1973 (Date nf i
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Scott D. Thayer
Kenneth Axetell, Jr.
8. PERFORMING ORGANIZATION REPORT NO.
Consultant
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Geomet, Inc.
50 Monroe Street
Rockville, MD 20850
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-02-1094
12. SPONSORING AGENCY NAME AND ADDRESS
Office of Air Quality Planning & Standards
Environmental Protection Agency
Research Triangle Park, N.C. 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
A general methodology is presented for relating traffic behavior parameters of
shopping centers, including peak trip generation rates and vehicle running time,
to more readily available characteristics of shopping centers, including average
trip generation rates and parking, exit, and entrance capacities. Such relationships
are to be used to relate shopping center characteristics to air quality.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COS AT I Field/Group
Air^Pollution, Urban Planning, Traffic
Engineering, Transportation Management,
Transportation Models, Land Use, Highway
Planning, Urban Development, Urban Trans-
portation, Regional Planning, Shopping
Centers, Vehicular Traffic
Indirect Sources
Indirect Source Review
13 B
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-65-
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