EPA-450/3-74-003-b
August 1973
VEHICLE BEHAVIOR
IN AND AROUND
COMPLEX SOURCES
AND RELATED COMPLEX
SOURCE CHARACTERISTICS
VOLUME II - AIRPORTS
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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EPA-450/3-74-003-b
VEHICLE BEHAVIOR
IN AND AROUND
COMPLEX SOURCES
AND RELATED COMPLEX
SOURCE CHARACTERISTICS
VOLUME II - AIRPORTS
By
Scott D. Thayer
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 con-
clusions expressed are those of the author and not necessarily those
o± 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. ŁPA-450/3-74-003-b
11
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ABSTRACT
The report presents an application of 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 a
broad quantitative fashion for the second of seven types of complex sources,
airports; the information generated, relating airport parameters to the
associated traffic behavior, will now be used by the sponsor to generate
guidance for studying the impact of new airports on air quality. The point
is made, however, that the development of a major new airport, or of a major
addition to an existing airport, represents an event of sufficient rarity
and magnitude, that it will inevitably require an extensive and intensive
environmental impact study of its own, including major concepts in land
development. A significant part of this must be a detailed study of all
emission sources associated with the airport, including vehicles. Such
a technique for vehicular sources is cited as part of a complete method-
ology being developed separately as part of the ongoing work of another
EPA contractor, Argonne National Laboratory.
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CONTENTS
Abstract i^j
List of Figures v
List of Tables vi
Sections
I Conclusions 1
II Recommendations 2
III Introduction 3
IV Characteristics of Airports 8
V Airport Parameters 16
VI Traffic Parameters 35
VII Analysis 43
VIII Results 51
IX Data Sources 64
iv
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FIGURES
Page
1 Schematic representation of vehicle operating modes at an 37
airport.
2 General relationship between traffic volume and total running 41
time.
3 Generalized methodology. 52
4 Generalized methodology applied to airports. 53
5 Isopleths (m x 102) of mean summer afternoon mixing heights. 59
6 Isopleths (m sec'1) of mean summer wind speed averaged through 60
the afternoon mixing layer.
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TABLES
No. Page
1 Airports with FAA-operated airport traffic control towers 10
by rank order of total aircraft operations - calendar 1971.
2 Airports with FAA-operated airport traffic control towners by 11
rank order of itinerant aircraft operations - calendar 1971.
3 Airports with FAA-operated airport traffic control towers by 12
rank order of air carrier operations - calendar 1971.
4 Airports with FAA-operated airport traffic control towers by 13
rank order of general aviation itinerant operations -
calendar 1971.
5 Estimated total airport populations average day (1966-67). 21
6 Estimated total airport populations peak day (1966-67). 22
7 Peak travel hours between airports and central business 25
8 Number of trips per hour for transportation services between 27
" airports and central business districts during peak hours/
off-peak hours^.
9 Vehicle trips related to passenger departure. 28
10 Airport employment related to passenger departures 29
11 Air carrier activity at selected airports - calendar year 1970 - 31
annual and monthly data.
12 Day-of-week variation of air carrier activity as percent of 32
daily mean - average of major U.S. airports.
13 Hour-of-the-day variation of air carrier activity as percent 32
of daily mean - average of major U.S. airports.
14 NREC aircraft classes, types, and average number of passenger 33
seats.
VI
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TABLES
No. Page
15 Distribution of air carrier activity by NREC aircraft class 33
(calendar 1969) and estimated numbers of automobile trips
per passenger seat.
16 Airport employee diurnal arrival and departure pattern 34
17 Vehicle exhaust emissions at idle in grams per minute , 36
18.a Base running times by operating mode at three Washington, 39
D.C. airports.
18.b Traffic model half-cycle counts for example hours assuming 46
employees and passengers are single half-cycles, and
visitors are full-cycles, or two half-cycles each.
19 Example queue calculations when gate capacity is exceeded. 49
20 Key to stability categories (after Turner 1970). 58
vii
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SECTION I
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 second (airports)
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 broad guidance for relating the traffic
characteristics of airports to typical and peak air pollution concen-
trations; however each new major airport or addition to an existing
airport will require special additional detailed analysis in its own right.
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SECTION III
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 development
(complex source) on air quality, it is necessary that traffic characteristics
associated with the source be identified and related to parameters of the
development which can be readily identified by 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 Air Pollution Technical Document
which will provide guidance to enable control agencies to relate readily
identifiable characteristics of complex sources to air quality.
The work is being performed in seven sub-tasks. Each sub-task is devoted
to examining vehicle behavior and its relationship to readily obtainable
parameters associated with a different variety of complex source. The
seven categories of complex sources are:
1. Shopping centers (15 Aug. - Report submitted)
2. Sports complexes (stadiums) (next sub-task)
3. Amusement parks
4. Major highways
5. Recreational areas (e.g., State and National Parks)
6. Parking lots (e.g., Municipal)
7. Airports (the present sub-task report)
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This, the second task report, describes the methodology developed, and
the analysis and results of its application to airports.
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 success-
fully 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 (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 seeding 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 parameteric 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 (e.g., 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 "(vehicle 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 Section III 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 covers special considerations required in the
case of airports, and describes the implementation of this methodology
for airports, and the results obtained.
SPECIAL CONSIDERATIONS FOR AIRPORTS
The construction of a major new airport, or the construction of a major
addition to an existing airport, is of sufficient significance and rarity
that it customarily receives extensive preparatory study in its own right,
including complete environmental impact analysis. This is required because
such a development involves not only extensive aircraft activity and
non-aircraft airport activity which are sources of pollutant emissions,
but will frequently generate associate land development which will in turn
involve additional emission sources.-
Reflecting the import and impact of such a development, the Environmental
Protection Agency is presently sponsoring the development, under contract
to the Argonne National Laboratory, of a complete methodology to enable
airport, transportation and comprehensive planners to incorporate environmental
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considerations into the site selection and design of airport facilities,
and into planning for the development of the land in the airport environs.
The portion of the Phase I report (air pollution) which deals with access
traffic is called to the reader's special attention, in order to demonstrate
the magnitude and complexity of a complete and proper assessment of
emissions from access traffic alone.
This is not to say, however that there is not a need for a general set of
guidelines which comprise a simpler version of the methodology, and which
treats, in simplified form, those aspects of traffic behavior which form
the major portion of the pollutant emissions. Accordingly, the generalized
methodology developed in the first of this series of reports has been imple-
mented here to provide broad guidelines which can be used to define the
range of emissions to be expected from a given airport development.
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SECTION IV
CHARACTERISTICS OF AIRPORTS
Broadly speaking, the most fundamental parameter which governs the char-
acteristics of an airport in determining its total emissions is that of
aircraft activity, or aircraft operations (also an indicator of passenger
and hence traffic numbers). This is not to say that aircraft are the
prime emitters, out rather that their number and type largely determine
all the other activity which involves emissions, including passenger
access traffic.
Other characteristics, such as the distribution of aircraft between air
carrier and general aviation, are of secondary importance, but on occasion
may be significant, as for example when most of the activity is general
aviation, with considerably less passenger access traffic.
FAA data, focussed on aircraft operations recorded at airports which have
FAA-operated airport traffic control towers, provide a broad base of
operational data for our purposes.
As of 1972 there were 12,070 civil airports of all kinds existing in the
United States; of these the National Airport System identified 3,240
in the National Airport System. FAA Air Traffic Control Towers operated
at 346 principal airports, and reported a total of 53,702,396 aircraft
operations, an operation being defined as either an aircraft arrival or
departure. Of these, 33,371,852 were so-called itinerant operations,
and thus of potential interest as regards ground traffic. The other type,
called local, involves operations wfthin the local traffic pattern or
in sight of the tower, flights to or from local practice areas, or
simulated instrument approaches, or low passes at the airport; thus the
local operations will generate relatively little associated ground traffic.
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The 33,371,852 itinerant operations were largely general aviation (22,093,762),
next air carrier (9,791,525) and last military (1,486,565).
The top ten airports in each of the four categories of total operations,
itinerant operations, air carrier operations, and general aviation itinerant
operations, are given in Tables 1 through 4, along with some statistics
related to the total sample of 346 control tower airports.
These tables demonstrate for us some order of magnitude ideas about aircraft
operations as indicators of the magnitude of activity at the principal
airports in the United States. In our subsequent treatment we will
emphasize the category of air carrier operations (Table 3) because these
generate by far the largest amount of associated ground traffic, from all
points of view (passengers, visitors, employees, ground service vehicles,
and cargo vehicles). The same principles to be developed may be applied
in instances where general aviation is of special interest, hut we will
not do so here.
Other characteristics of airports which are of potential relevance include
public traffic generated by air passenger activity and employee traffic.
Each airport may be expected to have distinctive traffic generated
characteristics for each traffic type. Public traffic generation varies
with the relative amount of through and plane-to-plane transfer passengers
(compared to originating and terminating passengers), and with the various
ground transportation modes employed. THese, in turn, are functions of
such complex variables as geographic location, city size, type of population
served, and available transportation systems. Employee traffic generation
is partially related to air passenger travel, but also is strongly dependent
on the amount of aircraft maintenance, air cargo activity, and other airport
services provided. Further information on these and other parameters is
provided in Section V titled Airport Parameters.
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Table 1. AIRPORTS WITH FAA-OPERATED AIRPORT TRAFFIC CONTROL TOWERS BY
RANK ORDER OF TOTAL AIRCRAFT OPERATIONS - CALENDAR 1971
Tower Rank Total
Number of Operations
Chicago O'Hare, 111. 1 641,429
Long Beach, Cal. 2 587,845
Van Nuys, Cal. 3 562,030
Santa Ana, Cal. 4 555,897
Los Angeles, Cal. 5 493,234
Atlanta Mun., Ga. 6 438,704
San Jose Mun., Cal. 7 408,252
Dallas Love Field, Tex. 8 387,092
JFK International, N.Y. 9 380,000
San Francisco, Cal. 10 366,766
Sum of Top Ten: 4,821,249(% of total: 9.0)
Total Airports: 346 Total Operations 53,702,396
Minimum Operations: 13,721
Maximum: 641,429
Median: 132,523
Mean: 155,209
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AMn FM-°PERATED AIRPORT TRAFFIC CONTROL TOWERS BY
RANK ORDER OF ITINERANT AIRCRAFT OPERATIONS - CALENDAR 1971
Tower
Chicago O'Hare, 111.
Los Angeles, Cal .
Atlanta Mun. , Ga.
Dallas Love Field, Tex.
JFK International , N.Y.
San Francisco, Cal .
LaGuardia, N.Y.
Miami, FT a.
Washington National , D.C.
Denver, Colo.
Rank
1
2
3
4
5
6
7
8
9
10
Number of Itinerant Operations
640,964
487,947
428,708
385,697
380,000
366,744
363,469
337,125
327,992
31?.fi73
Sum of Top Ten: 4,031,319 (%of total: 12.1)
Total Airports: 346 Total Itinerant Operations: 33,371,852
Minimum Itinerant Operations: 5,307
Maximum: 640,964
Median: 72,993
Mean: 9M50
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Table 3 AIRPORTS WITH FAA-OPERATED AIRPORT TRAFFIC CONTROL TOWERS BY
RANK ORDER OF AIR CARRIER OPERATIONS - CALENDAR 1971
Tower
Chicago O'Hare, 111.
Atlanta Mun. 5 Ga.
Los Angeles , Cal .
JFK International , N.Y.
LaGuardia, N.Y.
San Francisco, Cal .
Dallas Love Field, Tex.
Mi ami , Fl a .
Washington National, D.C.
Boston, Mass.
Rank
1
2
3
4
5
6
7
8
9
10
Number of Air Carrier Operations
565,826
387,775
373,870
333,558
287,192
286,339
270,573
233,958
222,739
213,594
Sum of Top Ten 3,175,424 (% of total: 32.4)
Total Airports with any air carrier operations: 296
Minimum Air Carrier Operations: 1 Total Air Carrier Operations: 9,791,525
Maximum: 565,826
Median: 10,798
Mean: 33,079
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Table 4. AIRPORTS WITH FAA-OPERATED AIRPORT TRAFFIC CONTROL TOWERS BY
RANK ORDER OF GENERAL AVIATION ITINERANT OPERATIONS - CALENDAR 1971
Tower Rank Number of General Aviation
Itinerant Operations
Van Nuys, Cal. 1 306,257
Long Beach, Cal. 2 270,322
Santa Ana, Cal. 3 247,107
Phoenix, Ariz. 4 194,188
Fort Lauderdale, Fla. 5 194,060
Houston, Tex. 6 189,683
Opa Locka, Fla. 7 181,970
Seattle Boeing, Wash. 8 172,482
Teterboro, N.J. 9 163,835
San Jose Mun., Cal. 10 159,123
Sum of Top Ten: 2,079,027 (% of total: 9.4)
Total Airports: 346 Total Gen. Av. Itinerant Operations: 22,093,762
Minimum Gen. Av. Itinerant Oprs: 244
Maximum: 306,257
Median: 54,200
Mean: 63,855
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FUTURE EXPECTATIONS
An EPA-sponsored study by the Argonne National Laboratory reports that,
according to the FAA, in the next ten years there will be a need for
1,410 new airports, of which 112 will accomodate both air carrier and
general aviation activity, the remaining 1,298 will be exclusively oriented
toward general aviation.
This indicates some 112 new airports which will have varying ratios
between air carrier and general aviation activity, and a wide range of
total activity. The key point to be made here is that significant new
airports will be increasingly rare events, and each, in its own right,
will warrant and receive extensive and intensive environmental analysis.
Accordingly, che analysis and methodology development reported here
focusses on broad guidelines for assessing environmental traffic impact,
and does not attempt to explore the refinements which may well be
essential in a detailed environmental impact analyses.
Further future expectations for large airports and/or airport modifications
are reflected both in the predictions from the FAA projection, and in
reports presented in forecasting architectural literature which attempts
to anticipate future airport requirements.
These respectively indicate both an increasing recognition of the potential
impact of airports on the environment, and an acknowledgement of the
need to perform detailed environmental impact analyses in order to both
account for the environmental impact and respond to the increasingly
restrictive legal requirements.
In broad terms, the specific major developments which are expected in
the foreseeable future are fairly readily identifiable. Reviews given
in recent architectural publications highlight key new construction
and expansion, such as Dal las-Fort-Worth, Tampa, Orlando, San Francisco,
Boston, John F. Kennedy, Jacksonville, Cleveland, St. Louis, Chicago
O'Hare, Kansas City International and Greater Pittsburgh. In addition
many of these point out special attention which must be given to,
and some solutions for, unusual problems in highway traffic, congestion
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and parking. In the Architechtural Record, Simon V. Waitzman, Vice President
of Airport Systems Planning and Design at John Carl Warnecke & Associates
is quoted, in part, as follows: "The airport planning team, including
the architect, cannot propose parochial solutions, but must be cognizant
of the needs of commercial and general aviation, of the severity of the
noise problem, of the demographic, ecological and pollution problems, of
the airspace/airport aircraft congestion problem, of the highway access
problem, and of the overriding problems associated with the premature
obsolescence of facilities."
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SECTION V
AIRPORT PARAMETERS
We now must convert what we know about airports into data which can be
quantitatively related to the ground traffic characteristics of the air-
port. Ideally this would be the traffic parameters themselves (numbers,
types and timewise distributions), but more often the information will
be in less direct form, such as passenger numbers, or passenger/visitor/
employee numbers; or even less direct, such as aircraft operations. Each
of these elements is relatable to each other by various approximation
techniques, and in addition there is seasonal, daily and diurnal data on
certain parameters which is summarized in the following material.
SCHEMATIC LAYOUT
To facilitate analysis of certain aspects of the problem, we need a diagram
of the airport, to include its access roads, parking areas, and curb front-
age for^enplaning and deplaning. Diagrams such as are found in the Air-
line Guide Travel Planner and Hotel/Motel Supplement are useful in this
regard. The latest issue has diagrams which present general access road
and parking layouts, and curb frontage access, as well as airline terminal
and gate locations, for some thirty of the major airports in the contiguous
United States. These provide a good picture of the tendency toward a simi-
larity of structure of these facilities. Parking is generally centrally
located, surrounded on three sides by the main terminal buildings (from
which the concourses project outward), and with the access roads located
on the fourth side. Characteristically, required new parking is added
along the access roads, where space is usually available because of the
normally remote location of the airport. Satellite lots with free shuttle
buses are also used. Future plans for some of the larger airports call for
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the use of multi-storied parking garages (up to six levels) handling
some 3,000 to 10,000 parking spaces; such structures generally do not
exist at present, and the size is of course constrained by height re-
strictions on near-airport construction.
Another tendency which is on the increase is the construction of com-
mercially oriented buildings (offices, hotel/motel structures, restaurants,
and similar structures) in the vicinity of airports; these have their own
associated traffic characteristics. We will focus, however, on the air-
port-oriented passenger and employee traffic.
Section IV has given some broad indications of airport activity. Other
broad indicators can be cited from various sources: for example, the
rate of increase in numbers of passengers served at major city airports
is beginning to flatten, although the rates at some medium-sized air-
ports continue to grow undiminished as they absorb some of the congestion
from the major airports. The rate of increase is slower on a national
basis. In spite of this, O'Hare repcrts a three-year passenger increase
through 1972 of from 18 million to 30 million annual passengers. Tampa
International has 3 million annually and is projecting 18 million by 1980.
Another indicator is in terms of present and projected gates. Dallas-
Fort Worth expects to eventually be three times the size of Kennedy, going
from 105 gates to almost 200. Orlando has 32 and forecasts 75 to 88, and
San Francisco projects an increase from 54 to 94. As shown by these ex-
amples, growth is definitely projected at many locations.
Characteristic parking lot sizes are in the range of 2,000-3,000 spaces
for airports such as Kansas City, San Francisco, Friendship, Dulles and
Washington National. Except for airports which are severely constrained
for space, of which the best (or worst) example is centrally located
Washington National, additional space as needed is not a problem; the
problem is the provision of parking'which is handy to the terminal by
one means or another, including shuttle. The only impact this has on
the present analysis is the tendency of drivers to preferentially seek
the close-in spaces.
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With regard to transportation access, private auto and taxi appear to
be the preferred mode; public transportation systems are in the study
(Dulles), planning (O'Hare), and initial (Washington National) stages
in a few places, but will have little impact on general requirements
for auto transport for some time to come.
Data on auto transport, passenger distributions, and aircraft activity,
all from a variety of sources listed in Section IX,are presented next.
The Technical Council on Urban Transportation reports the following,
from a survey of the following thirteen airports:
Atlanta Airport
Chicago-01Hare International Airport
Denver-Stapleton International Airport
Kansas City Municipal Airport
Los Angeles International Airport
Miami International Airport
New York-Kennedy International Airport
New York-LaGuardia Airport
New York-Newark Airport
Phoenix-Sky Harbor Airport
San Diego International Airport
Seattle-Tacoma International Airport
Washington, D. C.-National Airport
The 13 airports that are included in this study include many of the na-
tion's major airports. Using total enplaned passengers during fiscal
year 1966 as the basis of measurement, the list included the 4 largest
airports, 7 of the top 10 and 9 of the top 15. The airports are well
balanced geographically, covering 12 states and the District of Columbia,
and ranging the nation from Seattle to San Diego and New York to Miami.
All types of urban environments are represented, from densely populated,
highly complex areas, such as New York and Chicago, to relatively less
complicated areas like Phoenix and San Diego.
Ten airports provided complete data pn airline passenger, employee, and
visitor population for both average and peak days in 1966-67. Population
collectively totalled 475,000 people on an average day, and increased by
35% to about 650,000 people on a peak day. In approximate terms, airline
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passengers accounted for nearly one-half of the airport population,
employees about one-fifth, and visitors better than one-third.
The number of daily access trips made to and from the airport is differ-
ent than the reported airport population. Airline transfer passengers
are usually not involved in access to and from the airport. Further,
each airline passenger accounts for a trip either to or from the airport
whereas each employee and visitor accounts for a trip both to and from the
airport. On these bases, it is estimated that the total number of daily
ground access trips to and from the 10 airports referenced is nearly 50%
greater than the populations cited. Viewed in this context, airline
passengers account for only one-quarter of the access trips, as do em-
ployees. Visitors account for one-half of them.
The thrust of this report is central business district-to-airport access,
however. Central business districts can generate substantial, but usually
not majority proportions of total airport populations. Their influence
varies from city to city, but for six airports supplying complete infor-
mation on this matter, air passenger generation in central business
districts averages 29% of total airport airline passenger population;
corresponding employee generation is 11%; and visitor generation is 14%.
For all types of airport trips, central business districts generate an
average of 24% of the total airport populations.
Of the 13 airports participating in the survey, 3, Denver-Stapleton,
Kansas City Municipal, and San Diego, reported no access problems at
present. At Phoenix-Sky Harbor, congestion was only reported at the
airport entrance, and this is presently being corrected. The nine other
airports reported enroute congestion during peak hours, although only
moderate in two cases. Four of the airports do not have direct connec-
tions to freeways and this was generally cited as a problem, as was
congestion in front of airline terminal buildings at the airports, and
inadequate parking facilities.
Seven airports look to some sort of highway improvement to meet their
access problems. Improvements cited include new links to freeways,
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reconstructed enroute freeway interchanges for more efficient traffic
flow, and simply, additional highway capacity in terms of new or ex-
panded facilities. Many planners concur with the belief expressed by
the Los Angeles International Airport management that most airport
travellers will continue to use their private automobiles or other
roadway vehicles for the foreseeable future, and the practical answer
to serve this demand is to improve the capacity and quality of the high-
way network.
Table 5 shows the estimated total airport populations on an average day
in 1966-67. The data are presented in three categories: passengers,
employees, and visitors. Passengers include arrivals, departures, and
intra-airport transferees, i.e., arrivals who connect with departing
flights. Since they do not leave the airport, these transfer passengers
generally do not figure into the airport access problem. The number of
transfer passengers have not been identified in all cases, but they ac-
count for 10% to 30% of all airline passenger trips at the five airports
reporting this information.
Employees usually include airline operations and maintenance personnel,
Federal Aviation Administration flight controllers, airport staff, and
representatives of consumer services such as restaurants, gift shops,
rental car agencies, etc. Visitors are primarily relatives and friends
accompanying passengers, but in this report the term "visitors" also
includes sightseers and thos on airport business, such as salesmen,
service and repair personnel, etc.
For the 11 airports that have provided complete data for all 3 categories
of users on both average and peak days, the data reported show that approxi-
mately 475,000 people have been included in this survey of airport popu-
lation on an average day. Airline passengers account for 45% of the daily
airport population, employees 22%, and visitors 33%.
Table 6 shows the estimated total airport populations on a typical peak
day in 1966-67. These data are indicative of the average peak rather
than absolute peak conditions. Peak days can be seasonal in nature, but
-20-
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Table 5. ESTIMATED TOTAL AIRPORT POPULATIONS AVERAGE DAY (1966-67)
Airports
(1)
Atlanta
Chi cago-01 Hare
Denver-Stapleton
Kansas City Municipal
Los Angeles
Miami
New York-Kennedy
New York-La Guardia
New York-Newark
Phoenix-Sky Harbor
San Diego
Seattle-Tacoma
Washington, D.C.-
National
Passengers
(2)
29,600
50,000
5,500
6,700
42,000
22,000
46,800
17,200
14,000
6,000
3,000
10,000
26,000
Employees
(3)
12,000
16,000
5,500
1,100
33,000
5,000
23,000
3,300
3,300
300
1,600
4,000
13,100
Visitors
(4)
36,700
25,000
8,500
1,500
43,700
3,000d
22,800
4,000
4,200
8,400
3,200
4,700
26,000
Total0
(5)
78,300
91,000
19,500
10,300
118,700
30,000
' 92,600
24,500
21,500
14,700
7,800
18,700
65,100
^Total arrivals, departures, and intra-airport transferees.
.Data indicate employee counts on typical day. Total airport employee
population may be considerably higher due to flight crew rotations,
shifts, etc.
Data indicate total number of people at airports on typical day.
Total number of airport access trips are different however: each
passenger, with the exception of intra-airport transfers, accounts
for one single direction airport access trip per day. Each employee
and visitor accounts for two single direction airport access trips
dpertday.
Visitor traffic at Miami estimated to be low because of tourist-trade
nature of the airline passenger traffic.
-21-
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Table 6. ESTIMATED TOTAL AIRPORT POPULATIONS PEAK DAY (1966-67)
Airports
(1)
Atlanta
Chi cago-0 'Hare
Denver-Stapleton
Kansas City Municipal
Los Angeles
Mi ami
New York-Kennedy
New York-La Guardia
New York-Newark
Phoenix-Sky Harbor
San Qi ego
Seattle-Tacoma
Washington, D.C.-
National
Passengers9
(2)
59,200
60,000
6,900
9,000
52,500
31 ,900
58,500
18,400
15,500
6,500
3,900
12,000
33,000
Employees
(3)
14,400
16,500
5,500
1,200
33,000
5,500
23,000
3,300
3,300
3,700
1,600
4,000
13,100
Visitors
(4)
91 ,500
50,000
10,700
2,000
54,300
4,000
30,800
4,300
4,700
4,800
3,300
5,600
33,000
Total
(5)
165,100
126,500
23,100
12,200
139,800
41 ,400
112,300
26,000
23,500
15,000
8,800
21,600
79,100
See notes on Table 5
-22-
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regardless of their exact distribution they further accentuate peaking
during certain hours of the day as a general rule.
For the 10 airports that have provided complete airport population data
for both average and peak days, the average daily population of 475,000
grows to nearly 650,000 on peak days, an increase of 35%. The airline
passenger increase is 35% itself; the employee and visitor increases
are 2% and 55% respectively.
In the individial categories, the 45% airline passenger share of airport
population on an average day increases slightly to 46% on peak days.
The employee share of population drops from 22% on an average day to 16%
on a peak day, while the visitor share increases from 33% of total airport
population on an average day to 38% on a peak day.
Note that the number of passengers and visitors is approximately the
same at many airports, and in fact, for all 10 airports referenced,
passenger population is 1.38 times visitor population on an average day.
This factor is 1.20 on peak days. Business travellers are usually un-
accompanied to and from the airport while many nonbusiness travellers
are escorted by one or more relatives or friends, at least, at their
home airports.
Daily airport population differs from the number of daily airport access-
trips, however. Excluding intra-airport transferees, each airline pas-
senger accounts for one daily airport access trip - either into or out
of the airport. Employees and visitors each account for two daily trips -
both into and out of the airport. With this distinction in mind, and
assuming that intra-airport transferees amount to 15% of the total air-
line passenger traffic for the purpose of illustration, approximately
700,000 ground access trips are made to and from the 10 airports on an
average day (1966-67). On peak days this volume grows to about 950,000
reflecting the 35% increase that characterizes airport population on
peak days as opposed to average days.
Because each employee and visitor accounts for two access trips as cited,
their importance in access flow is more pronounced than would be indicated
-23-
-------
from a study of airport population alone. Whereas on an average day
visitor traffic constitutes 33% of airport population, it accounts for
45% of the access trips to and from the airport. Employees constitute
22% of the population, but 29% of the access trips. And correspondingly,
airline passengers who constitute 45% of the airport population only
account for 26% of the ground access trips to and from the airport.
Thus, visitors constitute the largest element in airport access traffic
on an average day - nearly one-half of the total flow - a'nd this position
is reinforced on peak days. Airline passengers and employees account for
the remaining one-half of the access traffic flow about equally.
Although this survey has not attempted to gage the magnitude of peak
period airport traffic flow, airport operators have been requested to
identify the timing of peak periods for central business district-airport
traffic. The responses, provided as a matter of record on Table 7, gen-
erally coincide with business hours on a normal work day.
The extent to which airline passengers (the following excludes employees
and visitors) travelling between the airport and central business district
use the various transportation services available shows that choices are
made on the bases of travel time, service frequency and connections, lo-
cation convenience, fare, comfort, and other amenities, some'of which ar.e
described in subsequent sections of this report. The data clearly show
that a great majority of the passengers use one of three means of trans-
portation: private automobile, taxi, or franchised airport bus or lim-
ousine. At the 10 airports providing complete information on modal choices,
the percentage of central business district passengers using these three
types of services ranges from 80% to 99%. Each of these specific services
carries at least 10% of the traffic at any one airport, with only two ex-
ceptions among the survey respondents. The only other services which
accommodate 10% or more of central business district passengers at any
airport are Newark, where frequent, nonstop public bus service is avail-
able in competition with the franchised bus, and at San Diego, where there
is no franchised bus service. Airports at Los Angeles and San Diego
-24-
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Table 7. PEAK TRAVEL HOURS BETWEEN AIRPORTS AND CENTRAL BUSINESS
Airports
(1)
Atlanta
Chi cago-01 Hare
Denver-Stapleton
Kansas City Municipal
Los Angeles
New York-Kennedy
New York-La Guardia
New York-Newark
Phoenix-Sky Harbor
San Diego
Seattle-Tacoma
Washington, D. C. - National
From
(2)
11:00 A.M.
6:00 A.M.
7:00 A.M.
6:30 A.M.
8:00 A.M.
4:00 A.M.
4:00 A.M.
4:00 A.M.
7:50 A.M.
7:00 A.M.
7:00 A.M.
9:00 A.M.
To
(3)
5:00 P.M.
9:00 P.M.
7:00 P.M.
9:00 P.M.
4:30 P.M.
8:00 P.M.
8:00 P.M.
8:00 P.M.
4:40 P.M.
9:00 P.M.
4:00 P.M.
4:00 P.M.
-25-
-------
specifically report that more than 1Q% of central business district
passengers use rental cars. It is assumed that at most airports,
rental care data is apparently included in with private car data. The
remaining services, helicopter and subway-bus transfer, account for an
insignificant share of total passengers.
Frequency of service is important to the extent that any scheduled trans-
portation operates often enough to assure ease of making plane connections
(Table 8). The public bus and subway-bus services are frequent urban tran-
sit operations, yet they control but a small part of the CBD-airport market.
Now, from a Traffic Quarterly paper by Louis E. Bender, we have extracted
the following information:
The amount of through air passengers and plane-to-plane transfer of air
passengers varies greatly. The New York airports are typical of pre-
dominantly originating and terminating air travel, and have negligible
through passengers (those who do not change planes). La Guardia and
Newark Airport have only 11-12 percent transfer air passengers, and
Kennedy has 23 percent transfers. In contrast, Atlanta has 10 percent
through traffic and 70 percent transfers,* as its location fosters through
passengers and transfers for air travel in many directions.
Airport visitors have been counted here as a portion of public-generated"
traffic. The bulk of these trips are occasioned by air passengers who
use private auto transportation but who do not leave an auto at the air-
port. Passengers were accompanied by airport visitors with the following
frequency: La Guardia, 47 percent, Newark, 53 percent, and Kennedy, 67
percent of the time. Thus, from one-half to two-thirds of auto users
generated travel-related visitors. The two-way nature of these trips,
as compared to the one-way trip of a departing passenger leaving an auto
at the airport, must be recognized in planning.
* Atlanta Airport Transportation Studies," Alan M. Voorhees Associates,
Inc., November 1968.
-26-
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Table 8. NUMBER OF TRIPS PER HOUR FOR TRANSPORTATION SERVICES
BETWEEN AIRPORTS AND CENTRAL BUSINESS DISTRICTS DURING
PEAK HOURS/OFF-PEAK HOURS9
Airports
(1)
Atlanta
Chicago-
O'Hare
Denver-
Stapleton
Kansas City-
Municipal
Los Angeles
Miami
New York-
Kennedy
New York-
La Guardia
New York-
Newark
Phoenix-Sky
Harbor
San Diego
Seattle-
Tacoma
Washington,
D. C.-
National
Airport
bus or
limo,
(2)
5/2
40/36
NA/20
NA
4/2
VI
-b
6/6
6/4
2/1
-
4/3
NA
Heli- Public
copter bus
(3) (4)
2/1
-
3/3
_ _
2/2
NA
2/1
1/1
2/1 10/6
3/1-1/2
3/3
NA 1/1
6/NA
Subway-
bus T .
transfer Iax1
(5) (6)
30/5
400/200
NA
NA
NA
NA
15/6 NA
15/6 NA
NA
NA
100/50
20/10
NA
Private
autos
(7)
NA
3000/100
NA
NA
NA
NA
NA
NA
NA
NA
NA
200/75
NA
Rental
cars
(8)
NA
270/50
NA
NA
NA
15/6
-
NA
bHours as designated on Table 7.
Typically 20 to 40 trips per hour, scheduled to connect with specific
flights.
Note: Data on taxis, private autos, and rental cars apparently reflect
the typical number of these vehicular movements into or out of the air-
ports so referenced.
-27-
-------
Usually, a minor portion of airport visitors' trips are not transporting
air passengers. At Kennedy, a study found that 15 percent of access
trips could not be accounted for by assignment to air passenger travel.
This was a measure of visitor trips for a host of reasons, such as:
ticket purchase, baggage movement, seeing off or greeting air passengers
but not transporting them, and purely casual sightseeing and dining.
Obviously, the amount of purely casual visitors will vary greatly among
airports; at a particular airport, it will be dependent on the airport's
relative and current attractiveness.
In summary, Table 9 has been developed to compare the vehicles generated
per 1,000 domestic departures at La Guardia Airport with the same rates
at O'Hare, Atlanta, and Los Angeles International airports. Vehicle
occupancies similar to New York experience have been assumed. From this
table can be seen the great difference in generation of air passenger
vehicles, as much as 3 to 1, because of differing characteristics.
Table 9. VEHICLE TRIPS RELATED TO PASSENGER DEPARTURE
Percent Passenger
Airport Transfer
to Other Plane
Percent Passenger Vehicles Per 1,000
Usage of Departing
Car or Taxi Air Passengers
La Guardia
O'Hare
Atlanta
Los Angeles
11
70
70
15
83
81
93
93
530
180
200
630
Employee traffic is the second principal component of surface traffic at
airports. It varies sharply depending on the concentration of air trans-
portation services such as plane maintenance, air cargo activity, and
food and other consumer services. The national airport survey found for
10 reporting airports that employees comprised 22 percent of the average
-28-
-------
daily population. The ratio in Table 10 of employee totals at the three
New York airports to air passengers shows how employee trip generation
can vary due to different levels of airport operating services.
Employee travel mode appears to differ little from that of the typical
office or industrial plant similarly located in the metropolitan area.
At Kennedy Airport, 90 percent of the employees use cars with a 1.1
occupancy (persons per car) and the remainder use buses. Because of the
large volume of employee-generated traffic, this segment of airport
traffic should not be underestimated. The employee traffic volume will
often exceed the traffic generated by air passengers.
Table 10. AIRPORT EMPLOYMENT RELATED TO PASSENGER DEPARTURES
Airport
1968
Total Employees
1968
Air Passengers
Employees Per
1,000 Annual
Air Passengers
La Guardia
Newark
Kennedy
6.589
6,870
42,522
10,481,999
6,716,504
19,573,628
0.63
1.03
2.17
The total traffic peak that will be experienced at airports will result
from the combination of public and employee-generated traffic. Auto-
matically recording traffic stations on the Van Wyck Expressway, the
main access route into Kennedy Airport, provide a 24-hour profile of in-
bound traffic, recorded at two locations: the Central Terminal Area
(CTA) boundary and the boundary of the airport. This permits a compari-
son of the CTA-generated public air passenger traffic with the total air-
port-generated traffic, the difference approximating employee-generated
traffic. Inbound data obtained on Friday, May 9, 1969, recorded a total
of 43,943 vehicles, of which 57 percent were destined for the CTA and
43 percent for the employee areas. In contrast to this predominance of
-29-
-------
air passenger traffic over 24 hours, during the 6 to 9 a.m. peak period
employee traffic outnumbered air passenger traffic nearly two to one and
in the 3 to 6 p.m. peak, employee traffic predominated 54 percent to 46
percent. The inbound peak hour from 7 to 8 a.m. totaled 3,829 vehicles,
of which 2,390 were employee vehicles.
The severest airport surface traffic volumes occur during the outbound
p.m. peak period. At this time, CTA traffic is at its daily high level
for a period of several hours, usually from 3 to 10 p.m. Within this
same period, from 3 to 6 p.m., the homeward employee peak produces com-
posite peaks from 3 to 6 p.m., approximately matching the time of the
off-airport peak. For example, on Friday, May 2, 1969, the Van Wyck
Expressway outbound flow was 10,901 vehicles between 3 to 6 p.m. with
the CTA generating slightly over half (5,766 vehicles). The outbound
peak hour, from 3 to 4 p.m., totaled 4,259 vehicles of which 2,158 were
employee vehicles.
The 1968 survey data show that 72 percent of air-passenger-generated
autos using Kennedy Airport desire to park, and the remaining 28 percent
enter and leave the airport without parking. Thirty-nine percent are
short-term parkers and one-third are "duration" parkers, leaving their
cars at the airport during their air travel.
In a study of airport pollution, Northern Research and Engineering cor-
poration generated data on airport operations, which have been converted
by us into the following summaries: Table 11, annual and monthly air
carrier activity; Table 12, activity by day of the week; Table 13, diur-
nal variations in activity; Table 14, NREC aircraft classes and seating
capacity; and Table 15, distribution of air carrier activity by class,
and estimated number of autos traveling per aircraft seat.
Finally, we have taken one table from the Argonne National Laboratory
Study cited in the section on Special Considerations for Airports, and
show it as our Table 16, on the diurnal distribution of employee traffic.
These data are subsequently employed in the Analysis Section (VII).
-30-
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Table 11. AIR CARRIER ACTIVITY AT SELECTED AIRPORTS -
CALENDAR YEAR 1970 - ANNUAL AND MONTHLY DATA
Airport
Total Passengers
Total Operations
(Landings and Takeoffs)
Air Carrier Operations
(Landings and Takeoffs)
Percent of Operations
by Month: Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov
Dec
Average Number of LTO
cycles per month
Average Number of
Passengers per
LTO cycle
Washington
National
10,124,423
333,548
219,550
8.2
7.7
8.9
7.9
8.6
9.1
8.6
8.4
8.1
8.4
8.0
8.2
9,148
92
Los Angeles
20,780,718
544,073
407,866
8.8
7.8
8.5
8.1
8.6
8.9
9.1
6.7
8.7
8.6
7.9
8.2
16,994
102
John F.
Kennedy
18,953,500
438,250
377,500
9.0
7.3
7.9
7.1
7.9
8.5
9.7
9.6
8.9
8.5
7.6
8.0
15,729
100
Chicago
O'Hare
28,936,000
679,750
628,500
9.1
8.2
8.5
7.2
8.8
9.1
8.8
8.8
8.0
7.9
7.5
7.8
i
26,186
92
-31-
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Table 12. DAY-OF-WEEKVARIATION OF AIR CARRIER ACTIVITY
Day of Week Sun
Percent of Daily Mean 90
Mon
104
Tue
104
Wed
104
Thur
104
Fri
105
Sat
87
Table 13.
pERCENnFTWr
PERCENT OF THE
CARRIER
FOR AN AVERAGE DAY
Hour
Percent of Daily Total for Four Airports
Washington . ...
National Los An9eles John F. Kennedy 'Chicago
J O'Hare
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
0.15
0.2
0.1
0.15
0.1
0.1
0.65
3.9
6.5
7.1
6.5
6.4
6.5
6.0
6.3
6.9
7.2
7.2
7.1
6.0
5.2
5.5
3.3
0.9
4.0
2.0
1.0
0.3
0.5
0.3
0.9
2.0
5.0
6.0
6.0
6.0
7.0
6.0
6.0
5.0
5.0
5.0
5.0
6.0
5.0
7-.0
5.0
4.0
0
0
0
0
0
2.2
3.6
4.7
5.3
5.7
4.4
4.8
4.7
4.9
6.4
7.3
7.4
7.7
6.6
7.7
6.6
5.9
4.1
0
0
0
0
0
0
1.5
3.9
5.1
6.0
6.2
5.9
5.7
6.4
6.7
6.9
6.6
7.5
7.2
7.2
6.2
5.0
3.5
2.5
0
-32-
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Table 14. NREC AIRCRAFT CLASSES, TYPES, AND
AVERAGE NUMBER OF PASSENGER SEATS
Aircraft Class
Passenger Seats
Aircraft Type
1
136
SST
2
490
Jumbo
3
129
Long-
4
116
Medium
5
61
Turbo-
6
10
Busi-
7
1
Piston
Jet range Range Prop ness Engine
Trans- Jet Jet Trans- Jet Utility
port Trans- Trans- port
port port
Table 15. DISTRIBUTION OF AIR CARRIER ACTIVITY BY NREC
AIRCRAFT CLASS (CALENDAR 1969) AND ESTIMATED
NUMBERS OF AUTOMOBILE TRIPS PER PASSENGER SEAT
Ai rcraf t
Class
3
4
5
Percent of Each Class by Airport
Los Angeles Washington John F.
International National Kennedy
69 0 55
25 70 40
6 30 5
Chicago
O'Hare
29
60
11
Estimated Auto-
mobiles per
Aircraft Passen-
ger Seat
1.2
1.0
1.1
1.1
-33-
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Table 16. AIRPORT EMPLOYEE DIURNAL ARRIVAL AND DEPARTURE PATTERN
Hour
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1.5
16
17
18
19
20
21
22
23
24
Percentage of
Employees
Arriving
0.1
0.1
0.3
0.3
1.3
5.3
27.3
19.1
4.5
2.8
3.0
1.7
4.0
5.6
8.8
4.0
1.9
0.5
1.3
1.0
0.9
0.3
5.0
0.9
Percentage of
Employees
Departing
3.0
0.5
0.7
0.3
0.2
0.4
1.0
3.5
1.3
2.0
2.2
3.5
1.2
3.0
6.1
25.0
14.9
6.0
3.6
3.0
3.0
2.8
5.0
7.8
100.0%
100.0%
-34-
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SECTION VI
TRAFFIC PARAMETERS
CONCEPT OF EMISSIONS PER UNIT TIME
In parking areas of airports, maximum vehicle speeds rarely exceed 10 or
15 mph, and average speeds are much lower. The usual procedure for esti-
mating motor vehicle emissions as a function of vehicle speed is not
very accurate at these low speeds due to:
a. Difficulty in estimating average operating speed; and
b. Extreme variation in observed emission rates per unit distance
traveled with slight change in average operating speed.
For airports, analysis shows that traffic operations and their related
emissions are better considered in units of time (grams/minutes) rather
than units of distance (grams/mile), for the following reasons:
1. The variations in emission per unit time at different speeds are
relatively insignificant at the lowest speeds;* and
2. Traffic movement in and near the vicinity of an airport can 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 at idle are
available from A Study of Emission from Light Duty Vehicles in Six Cities.**
They are summarized in Table 17. These test data compare well with emission
factors calculated from the current edition of AP-42,*** when converted
to grams/minute at various speeds an'd then extrapolated to zero speed.
* Less than 50 percent increase from idle to 15 mph.
** Reference: Automotive Environmental Systems, Inc., March 1973.
Environmental Protection Agency Publication No. APTD-1497.
*** Reference: Compilation of Air Pollutant Emission Factors. April 1973.
Environmental Protection Agency Publication No. AP-42 (Second Edition)
-35-
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Table 17. 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 determine
an associated running time or emission period for the modes.
In applying the recommended procedure of emission estimation, total
vehicular emissions from the airport 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 Traffic Modes for Airports
For purposes of analysis, traffic movement in the vicinity of an airport
has been divided into eight characteristic operational modes. Emphasis
here is on private auto use. Comments are made later regarding other
transport. The modes are summarized below and shown schematically in
Figure 1.
We have distinguished the three major types of access traffic from each
other in the figure, and discuss this distinction later in this section.
The discussion which follows treats principally visitors/passengers who
park, and employees (who also park, but most often in a separate lot).'
-36-
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Access Road
Key: Visitors/Passengers who park
Visitors/Passengers not parking;
also Taxi/limousine/bus
Employees
Figure 1. Schematic representation of vehicle operating
modes at an airport.
-37-
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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 airport.
Entrance (I) - Movement through the entranceway, 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
terminal entrances. Time spent searching for an open space is also included
in this mode.
Stop (S) - Parking of the vehicle and shutoff of 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 airport.
The average running time in each of these modes can be quantified for a
specific airport as a function of its physical dimensions, traffic control
devices, and traffic volume.
The third category of access traffic, distinguished from the two covered
above (visitors/passengers, and employees, who park in lots), is that of
some of the visitors/passengers, and all of the taxis, limousines and busses,
all of which come to the main terminal entrances, stop and idle, or stop
and shut off engines, for varying periods of time, and then depart. Differences
from the modal descriptions above are in the absence of the entrance and
exit modes, and the importance of the stop/start mode, which may involve
extended periods at idle, without shutoff and start of the engine.
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Base Running Time
There is an average minimum vehicle running time for each airport 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 for the airport and
because at most airports 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 airport, with an additional knowledge of its traffic
control devices and probable driving patterns.
Base running times for three example airports, Washington National,
Baltimore Friendship, and Dulles International, have been constructed
both by time measurement during simulated driving cycles and by estimates
based on dimensions of the airports, entrance/exit configurations, and
expected driving patterns. Total base running times and average times in
each operational mode are shown in Table 18.
Table 18. BASE RUNNING TIMES BY OPERATING MODE AT
THREE SUBURBAN WASHINGTON, D.C. AIRPORTS
Operational Mode
Approach
Entrance
Movement in
Stop
Start
Movement out
Exit
Departure
Total BRT
Base Running Time, Minutes
Dulles
International
1.0
0.25
2.0
0.1
0.1
0.75
0.75
1.0
5.95
Washington
National
1.5
0.25
1.5
0.1
0.1
0.75
0.75
1.5
6.45
Friendship
International
2.0
0.25
0.75
0.10
0.10
0.75
0.50
2.0
6.45
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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 parking ticket booths, traffic control lights and signs at
entrance/exits
e Queues created as vehicles attmept to exit onto uncontrolled access
roads
o 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
o Increased number of pedestrians in parking area.
Generally, total runnina time is Qualitatively related to traffic volume
as shown in Figure 2. The base running time (BRT) can be determined for
a specific airport as described above. The magnitude of increase above
the BRT with increased traffic can be approximated from airport and trip
generation parameters, by the procedure developed in the section titled
Analysis.
Identification of Critical Modes for Airports
Examination of the eight operational modes that were identified indicates
that for airports, running times in some modes are relatively constant,
but that times in others may increase from the base running time during
peak usage and traffic conditions. For airport parking facilities, the
three modes whose times are most affected by traffic congestion, 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 entrance/exit ways. As these capacities
are approached or exceeded, running times in the two modes increase.
-40-
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Gate or
Parking Capacity
Traffic Volume
Figure 2. General Relationship Between Traffic Volume and Total Running Time
-------
Waiting times in the resulting queues become the primary factors in determin-
ing total running times. However, because of variations in the number of
vehicles entering and leaving airport parking facilities egress and ingress
capacities generally are not exceeded simultaneously.
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 parking facility 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.
For non-users of parking lots, the stop/start mode may become critical
during periods of congestion, especially if extensive idling times develop
at main terminal entrances. This can happen in the case of visitors/
passengers who do not use the lots, and public transportation (taxis,
limousines and buses).
The parameters developed above are analyzed further with the airport
parameters in the Analysis section, and the findings employed in the
Methodology section. Also, in the Analysis section, distinction is
made where appropriate between the traffic characteristics of airports
and those of their parking facilities.
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SECTION VII
ANALYSIS
In this section we make the necessary interpretations and inferences for
converting the data of the section titled Airport Parameters into the
relationships needed for input to the methodology of the section titled
Results. In the section titled Traffic Parameters, we discussed the
entrance/exit and parking capacities, and the terminal curb frontage, as the
airport parameters which could, under conditions of exceedance, increass either,
or both, the vehicle running times and the number of vehicles running.
Typical and Maximum Trip Generation
There are two routes we can follow in getting to the necessary typical
and peak trip generation rate data, The first we use in cases where
direct data or estimates are available on frequencies of passengers,
employees and visitors for typical and peak conditions; these data are
directly convertible to estimates of vehicle numbers. In the absence of
such population numbers, then the alternate method proceeds from data
on aircraft operations to numbers of people, and thence to numbers of
vehicles.
First, if the data are people-oriented: it will probably be necessary
to convert aggregate data into smaller Increments. Annual data may, for
example, be reduced to monthly, daily and hourly figures (in order to
obtain typical and peak one-hour and eight-hour values) using the char-
acteristic monthly, daily and diurnaj data of Section V on Airport
Parameters.
.43-
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Of course, if the available data are already in the necessary time-period
forms, then the conversion to vehicle numbers may be done directly, again
using the factors of Section V on people-vehicle relationships.
The passenger estimates are divided between through and transfer passengers
on the one hand, and originating or terminating on the other. This
distinction, as well as identification of the numbers of employees and
visitors, provides the basic input. Conversion to vehicle numbers, and
combination with base running times, provides the data sought for this
subtask.
As an example, let us take an average and a peak day at JFK, from the
Transportation Engineering Journal (TEJ)-data, Table 5; for the average
day there are 46,800 passengers arriving, departing and transfering;
23,000 employees; and 22,800 visitors. The corresponding peak numbers
are 58,500 (indicated as averaŁe peak, rather than absolute peak);
23,000; and 30,800. From Table 11, the peak hour for visitors/passengers
is from 5 to 6 p.m. (hour 18) and reflects 7.7 percent of the total (the
peak eight hour period is from 2 to 10 p.m. and contains 55.6 percent.
For employees (Table 16) the peak hour is hour 16. This is discussed
further. TEJ assigns a complete trip cycle to each employee and visitor,
and a half cycle for each originating or terminating passenger, for long-
term (daily) considerations. For the employees, the diurnal pattern
(Table 16) indicates that the departing hourly peak has passed (3-4 p.m.)
when the passenger/visitor hourly peak occurs. We must now determine
the composite peak, since the employee and vtsitor/passenger hourly peaks
do not coincide, and the employee traffic is Targe in amount in this
case; it is always characteristically more peaked than the visitor/passenger
case.
We should now be concerned, however, about the half-cycle/full
cycle assignment of trips for short (one-hour) -terms, and must also be
concerned about through and transferring passengers (who do not involve
ground vehicle0), and the potential overlap of counts for visitors and
passengers, as regards vehicles. For employees, of course, we have
-44-
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from Table 16 the distinction between arriving and departing, so we
determine the numbers corresponding to the hourly percents, and assign
arriving and departing half-cycle counts, respectively. For the visitors/
passengers this is more complex, since some passengers are through or
transfering, some visitors do not transport passengers, and some passengers
"park and fly," or ride public transport. We have made some rather involved
sample calculations using data from Bender's Traffic Quarterly article and
the TEJ article, and find that, as a reasonable first approximation, the
assignment of half-cycles to passengers and full cycles to visitors, as
done in the TEJ article, accounts for the interactions adequately.
Table 18.a shows the result, giving us an interesting impression of the
interplay between employee and passenger/visitor traffic. (Table 18.a
for typical hours has also been calculated.) The employee peak hour
controls the time of the composite peak hour, and the invariance of employee
traffic prevents the relative increase in traffic under peak travel condi-
tions from soaring. Parallel treatment will give us the corresponding data
for the typical and peak eight-hour periods.
The somewhat complex approach described above for passenger/visitor vehicle
estimates, using passenger data, can be approximated by the use of air
carrier activity as promulgated by NREC.
We go to Table 11 and find that a typical month has 8-1/3 percent (1/12)'
of 188,750, or 15,729 air carrier LTO cycles per day, and the peak month
(July) has 18,309 (9.7 percent). Our peak hour is again the 18th (5-6 p.m.)
with 7.7 percent (Table 13). From Tables 14 and 15, we find the distribu-
tion of aircraft classes and associated passenger seats, with the following
results; 55 percent with class 3 (129 seats), 40 percent class 4 (116 seats)
and 5 percent class 5 (61 seats). This estimates that the average 100 LTO
cycles implies (55x129 + 40x116 + 5x61) or 12,040 passenger seats, and for
15,729 LTO's we have 1,893,722 passenger seats. Applying the factor of 1.1
from Table 15 implies passenger related 2,083,149 vehicle trips during the
month, or 67,198 per day. A typical hour has l/18th of these, or 3,733. Peak
hours of the peak month (hours 16, 17, and 18) each have over 7 percent
of the peak month's values (78,220 vehicle trips), or 5,710 (hour 16)
-45-
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5,788 (hour 17) and 6,023 (hour 18), respectively. We must now break
these out into half-cycles, as was done before, accounting for the fact
the visitors trips are full cycles and passengers only, half-cycles.
Table 18. Traffic Model Half-Cycle Counts for Example
Hours Assuming Employees and Passengers are Single
Half-Cycles, and Visitors are Full-Cycles,
or Two Half-Cycles Each
Hour of
the Day
Typical
(Total
Divided
by 18 Hours
of Operation
15
16
17
18
a. Typical Counts
Passengers
2,600
Visitors
2,533
Employees
1,278
Totals
6,411
b. Peak Counts
3,744
4,271
4,329
4,504*
3,942
4,496
4,558
4,744*
3,427
6,670*
3,864
1,495
11,113
15,437**
12,751
10,743
* Individual Peak
** Composite Peak
We do it first for the typical hour (3,733): Analysis of the data of Section V
(Airport Parameters) indicates that about half of the total (1867 out of 3733)
is half-cycle oriented, passenger-only, and the other half are full-cycle
passenger and visitor-oriented trips. Thus, we have (1,867 plus 1,867x2), or
5,601 half-cycle trips for passengers and visitors, approximating the first
two columns of Table 18.a, which total 5,133. We suggest use of this relation-
ship consistently.
Employees must be treated separately, and will, with necessary input from the
developer, using comparable data as was obtained before from Tables 5, column
b and Table 16, generate the same results as before in Table 18.a and Table
IB.b in the "Employees" column.
-46-
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The peaks for Passengers/Visitors are calculated in similar fashion to
those for typical hours. We get, for example, for hour 18, (3012 plus
3012x2), or 9035, again comparable to the total of 9248 from the first
two columns of Table 17.b, hour 18. The remainder of the treatment is
as before. We must take special note of the dependence of this scheme
(using air carrier operations) on the aircraft class/passenger seat
distribution. With the advent of the larger capacity aircraft (L-1011
and DC-10) the passenger seats per aircraft will increase significantly;
this must be accounted for in projections.
AIRPORT PARKING LOT GATE CAPACITY EXCEEDANCE AS A FUNCTION OF TRIP
GENERATION AND GATE CAPACITY - RESULTING RUNNING TIME INCREASE (E)
Average running times for entrance to and exit from an airport's public
parking lot are primarily functions of three parameters: traffic trips
in and out of the lot, 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
an airport lot, by use of a methodology employing queueing theory.
The entrance and exit capacities for an airport lot are each considered
to be constant over the time frame (one-hour) of this analysis. 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, and time sequences on parking fee collection at gates.
The total traffic entering or leaving the parking lot during any incre-
mental period (trip generation) can be determined from the data on hourly,
daily, and seasonal variations that were previously presented in the
section titled Airport Parameters, and from the section titled Analysis.
These data should, if possible, be adjusted to match the expected traffic
for each specific airport lot and may need to be further adjusted to
account for atypical variations at the lot, either anticipated or
observed.
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Estimates of running times for the entrance and exist 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 hr~ . where
\ /
a = utilization factor
traffic flow, veh/unit time
gate capacity, veh/unit time
b = average outflow time per vehicle (inverse of gate capacity),
min.
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 19. 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.
AIRPORT PARKING LOT PARKING CAPACITY EXCEEDANCE
This factor, which could be analyzed fairly effectively in the preceding
study (Shopping Centers - GEOMET Report No. EF-263) because of an Urban
Land Institute study on that specific element, did not lend itself to
quantification in the present study^at all. Accordingly, only the fol-
lowing general guidelines can be given.
First, the information available indicates that, except in special cases
like Washington National Airport, which is limited in space and cannot
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to
I
Table 19. EXAMPLE QUEUE CALCULATION WHEN GATE CAPACITY IS EXCEEDED
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 X5:30
5:20 6:00
6:00 6:30
N = nupi
2
Traffic
Vol ume
(in or out)
900
1220
1400
1600
V400
1100
980
750
IP lormtK in
3
Gate
Capacity
(in or out)
1200
1200
1200
1200
1200
1200
1200
1200
f i V»O
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
RT = average running time, in minutes
= (av. outflow time per vehicle, min.) (av. queue length)
-------
readily generate new parking areas, most airports should not suffer from
this problem. Developers of new airports, and expanders of existing ones,
can anticipate future parking requirements and allow for such expansion
by advance lot construction before the problem develops.
Second, the problem should be calculable, for those cases when the cal-
culations show that required spaces approach the parking capacity, by
simply increasing the "Movement In (MI)" time by an amount approximating
a slow movement, say, half-way around the lot, to reflect the search for
a space.
AIRPORT TERMINAL AREA CAPACITY EXCEEDANCE
We recognize that one problem which exists and should be quantified is
the situation when, during extreme peak periods, traffic congestion builds
up as vehicles attempt to get to the terminal's curb frontage access areas
for pickup and discharge of passengers. We have found no data on this,
and again can only suggest an approximate technique. The end result of
this phenomenon will be an increase in the idle or stopped time between
the "stop" and "start" modes for vehicles trying to perform this maneuver,
plus an increase in the time required for those vehicles to pass which are
simply trying to depart the terminal; in Figure 1, for example, this
would include employees who have left their lot and are departing past
the terminal curb frontage. Allowance for a stopped-idle time for the
first category, and for an increased departure time for the second, would
have to be estimated by the analyst for a specific case in point, based
on the configuration, curb frontage, road capacity and number of vehicles
involved per unit time in the two categories.
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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 3, 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 typical 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 airports, as shown in Figure 4, the methodology proceeds
from basic information about a given airport (see "Airport Parameters"),
via traffic behavior data (see "Traffic Parameters"), and typical trip
generation data (see "Typical and Maximum Trip Generation"), to generate
-51-
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J Characteristic \_
"1 Parameters I"
V of Complex /
\
n )
Generation/
Values, y
Exceedance\
Values
/^TypicaTX
( Trip )
\Generationy
v^-V.glur \y
Peak
Running )
Times
Peak Values
of Numbers of
Running, and-
Running
Timp
Basic
: Running
Time
( Running )
X^Time ^/
eak Values
of Numbers of
Running, and)
Peak Running
Times'
/Typical Values\
/ of Numbers of \
ICars Running, andy
\ Base Running J
Figure 3. Generalized Methodology
-52-
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Sec. V
Characteristic
Parameters of
Airports
1
VII ,,
ParkingX
Lot Gates \
and Gates )
.Capacities/
/TerminalN .,—
[Curb Front-\ /Employee \f Ajrcraft
\age and Roacjf ( Parameters I I Operations
Passenger
Data
Parking
Capacity
Analysis
VII
f Parking^*
Exceedance I
Schematic
Layout
VI
Traffic
Modal
Analysis
Peak Trip"\
Generation)
Values^/
/Gate\
IExceedance I
/Frontage \
I and Road )
VExceedance/
Typical
Trip Gen'
eration
'Typical Valuers
of Numbers of \
Cars Running, )
and Base Runn-y
Times ^/
VIII
'Teak Values
of Numbers of
[Cars Running, and)
Base Running
Ti.net
''Peak Values ~
of Numbers of
(Cars Running, and
Running
Limes
Figure 4. Generalized Methodology Applied to Airports
-53-
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estimates of typical numbers of vehicles and associated base running
times for one-hour and eight-hour periods; these are two of the required
end products of the task. For the peak case, peak trip generation rates
are estimated (see "Typical and Maximum Trip Generation") and then used
to obtain exceedance estimates for parking lot gate and parking capacities,
and for terminal curb frontage and road capacities (see the Analysis
Section). That section describes general ways to estimate 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 third possible combina-
tion is of course the case where peak vehicles do not create exceedances,
and thus are combined with base running times. The specifics of the pro-
cedure, with examples, are presented in the following paragraphs. It is
easiest done with the occasional use of examples, but the general applica-
bility will be evident.
First, we define our existing or proposed airport, or expansion, by means
of a schematic diagram (Figure 1), and available or estimated data on:
public and employee parking spaces, parking lot gates and gate capacities,
and terminal curb frontage and road capacity; also numbers of employees
and expected arrival and departure rates; also aircraft operation and/or
passenger data, in as much detail as is feasible. If any of these para-
meter values are uncertain, then the estimated range should be provided
(with an assist from some of the general data in the section on Airport
Parameters), 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 times
(Figure 1 and Table 17). Possible differences among base running times
should be delineated for the three major traffic types, which use the
public parking lot, use the employees' lot, or go to the terminal curb
front (Figure 1 and the section on Traffic Parameters). Table 17 shows
base running times of about six minutes for each of the three Washington
area airports.
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The trip generation data may be obtained by the procedures of the analysis
section, using employee data or estimates for the employee trips, and
starting from aircraft operations, or passenger data, or both, for estimat-
ing the passenger/visitor-related traffic. If both aircraft and passenger
data are available, it might be well to calculate both ways, for increased
confidence in the result. If the results conflict, then either more
analysis is called for, or the passenger technique should be accepted.
The identity of each of the three traffic types (employee, public parking,
and terminal) is preserved through the calculation because of the potential
differences in the modal elements of the base running times, and because
of the differential impact of exceedances on different modal times
(Analysis Section). Also, from the Analysis Section, if the time of con-
cern is short enough we include only half the cycle (entrance or exit,
not both) in the calculation of the employees and some of the passengers
("park and fly" types).
The base running times for each traffic type are combined with the typical
trip generation rates for the same type, to provide the required typical
values of vehicles running and base running times.
For the peak case, the diurnal variations of passenger/visitors and
employee vehicles must be examined to select the composite peak hour.
Typically the employees' trips show more exaggerated peaks than passengers/
visitors, so that in cases where there are relatively large numbers of
employees, their trips will determine the peaking time. Having defined
the peak time, we first combine its vehicle numbers with the base running
times to characterize that case where no capacity exceedances might exist.
We now proceed, for the peak case, to determine whether the peak vehicle
numbers represent exceedances of any of the critical elements of public
parking lot gate and parking capacity, or terminal curb frontage and road
capacity. The methods of the analysis section give general approaches
as to how to treat each of these potential exceedance areas, but the
specifics of each case will require interpretation and judgement on the
part of the analyst. An example of gate capacity exceedance is given in
the Analysis Section.
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The resulting increases in times are added to the appropriate base running
times to give the peak running times, the peak trip generation rates will,
as for the typical case, give the base peak numbers of vehicles running,
to which we add any additional vehicles running because of exceedances.
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 an airport complex can usually
be considered as being distributed fairly uniformly over the area of the
airport roads and parking areas during typical operating periods (base
running times), as indicated by the schematic in Figure 1, and the example
data in Table 17 (see the section titled "Traffic Parameters"). For most
analyses, an assumption of a geographically uniform emission density is
thus sufficiently accurate. It iv.ay be necessary to distribute the access
road traffic (and hence the Approach and Departure Model Emissions) along
the access roads, depending on their orientation (especially if straight
away from the airport) and distance of expected effect. Peak traffic
conditions can result in either the gate or the parking capacities 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.
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
source(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.
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If the terminal area capacity is exceeded, than the excess vehicles may
be simulated as a line lying along the terminal curb frontage, and treated
comparably to the parking lot queues described 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 20. For ground level
sources, such as automobiles at airports, the ground level concentrations,
both in the vicinity and downwind of the sources will be inversely pro-
portional to wind speed and mixing height and directly proportional to
atmospheric stability (i.e., the more stable the atmosphere, the higher
the concentration).
Peak use of airports occurs during major holiday periods, especially in
midsummer, with th" highest day of the week usually being on Friday. The
peak hour use gene,ally occurs during the mid- to late afternoon. The
peak eight-hour period is generally 2 p.m. to 1C p.m. Holzworth (1972)
has mixing height and wind speed figures which are directly applicable to
summer afternoon conditions for locations in the contiguous United States,
and these may be used directly (Figures 5 and 6). For the Friday after-
noon peak, atmospheric stability classes B, C, and D may occur with classes
C and D being the most prevalent.
The period when meteorological conditions are least favorable for diluting
pollutants is the period when airports are essentially not in use (Tables 13
and 16). This would be from very late in the evening until a few hours
after sunrise. It is most often during this period that mixing heights are
lowest, wind speeds are lowest, and atmospheric stability is greatest.
* This section was prepared by Mr. Robert C. Koch, Senior Research
Scientist of GEOMET, Incorporated.
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Table 20. KEY TO STABILITY CATEGORIES (after Turner 1970)
Day
Night
Surface Wind
Speed (at 10 m) ,
m sec"1
<2
2-3
3-5
5-6
>6
Incomi
Strong
A
A-B
B
C
C
ng Solar Radiation
Moderate SI
A-B
B
B-C
C-D
C
ight
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 dav
or night. a J
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
?J/2 Cle^ skies can be expected to be reduced to moderate with broken
U/8 to 7/8 cloud cover) middle clouds and to slight with broken low
clouds.
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12
I
en
10
18
Figure 5. Isopleths (m x 102) of mean summer afternoon mixing heights
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CT>
O
Figure 6 . Isopleths (m sec'1) of mean summer wind speed averaged through the afternoon mixing layer (Figure5).
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For most parts of the country, autumn is the season when these least
favorable conditions are most likely to occur.
If one now considers that operating hours for airports are generally
7-8 a.m. to 11 p.m., then, from a meteorological point of view, the
single hour least favorable for dispersing pollutants during that period
is from 11 p.m. to midnight during the autumn season. The least favor-
able eight-hour period would be from 4 p.m. to midnight; composite use
levels for airports during these periods can be derived as they were for
Table 17, from Tables 13 and 16. During these times, the mixing height
is usually estimated to still be at the afternoon value (Figure 5), not
having lowered to the morning value till after midnight; the wind speed
estimate is also still best given by the afternoon value (Figure 6).
However, as the time proceeds toward the latter part of the evening
period, stability classes D and E become prevalent.
QUALITATIVE GUIDELINES
In addition to the quantitative evaluation procedures developed above,
the review of airports as complex emission sources should also include the
following considerations which are not presently reducible to quantitative
terms:
1. Maintain the close-in public parking area as premium short-term
space (low rates for short-term, very high rates for longer term). Keep
valet and long-term parking in more distant and satellite lots, with free
and frequent shuttles. Maintain low long-term rates in more distant
lots to attract "park and fly" passengers. Use obvious and attractive
markings to guide traffic accordingly.
2. Have adequate numbers of parking lot gates and gate capacities, and
personnel and signs to guide parkers to lesser used exit gates.
3. Avoid traffic patterns that require left turn movement across
traffic flow.
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4. Maintain adequate terminal curb frontage for pick-up and discharge
of passengers, and adequate road capacity for through flow. Allow no
short-term method parking in this area, and use traffic control personnel
to prevent excessive congestion.
5. More strongly encourage development of public rapid transit systems
to airport terminal from city center and other major communities.
THE NINE QUESTIONS
While the specific information called for by the task work statement has
been provided in the sections from Airport Parameters through Meteorological
Aspects, the nine questions spelled out as part of the statement warrant
specific response. This is given here, with the questions abbreviated.
1. Area allotted to or occupied by a single vehicle? The area ranges
from 9 x 20 feet (180 ft?) to 10 x 20 feet (200 ft2).
2. Percentage of land and parking spaces potentially occupied by
vehicles? The usual percentage? As indicated in the sections entitled
Airport Parameters and Analysis, these data are only indirectly related
to our methodology. To the extent that they are relevant, they are
discussed in those sections.
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 Airport Parameters.
7. Design parameters in question (6) which can be most successfully
related to traffic, and hence emissions? See section titled Analysis.
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8. Relationships of parking lot design to parking densities and vehicle
circulation? What is typical design? Design with highest parking den-
sities, 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 Airport Parameters through the sections
titled Traffic Parameters, and Qualitative Guidelines.
9. Meteorological conditions likely to occur during peak use? Use
level during periods of worst meteorology? See section titled Meteorological
Aspects.
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SECTION IX
DATA SOURCES
BOOKS AND REPORTS
Norco, J.E., R.R. Cirillo, T.E. Baldwin, and J. W. Gudenas. An Air
Pollution Impact Methodology for Airports - Phase I. Argonne National
Laboratory. EPA Publication No. APTD-1470. January 1973.
Architectural Record. Building Types Study 413:
Vol. 148, No. 3. August 1970.
Architectural Record. Building Types Study 440:
No. 4. October 1972.
Airports and Terminals,
Airports. Vol. 152,
Technical Council on Urban Transportation. Survey of Ground-Access
Problems at Airports. Transportaion Engineering Journal, Proceedings
of the American Society of Civil Engineers. February 1969.
Bender, Louis E. Airport Surface Traffic Demands. Traffic Quarterly,
based on a paper presented at the Institute of Traffic Engineers
Annual Meeting, Long Angeles. August 1969.
Platt, M., R.C. Baker, E,K. Bastress, K.M. Chng, and R.D. Siege!. The
Potential Impact of Aircraft Emissions Upon Air Quality. Northern
Research and Engineering Corporation, Cambridge, Massachusetts.
December 1971.
Federal Avaiation Administration. FAA Air Traffic Activity - Calendar
Year 1971. Department of Transportation. February 1972.
Holzworth, George C. Mixing Heights, Wind Speeds and Potential for Urban
Air Pollution throughout the Contiguous United States. EPA Office of
Air Programs Publication No. AP-101. January 1972.
Official Airline Guide. Travel Planner and Hotel/Motel Guide. The
Reuben H. Donnelley Corporation, New York City. Summer 1973.
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PRIVATE COMMUNICATIONS
Information provided by management personnel of: Friendship International
Airport, Baltimore; Washington National Airport; Dulles International
Airport, Washington, D. C.
-65-
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-450/3-74-003-b
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
Vehicle Behavior In and Around Complex Sources and
Related Complex Source Characteristics
Volume II - Airports
5. REPORT DATE
August 1973 (Date of issue)
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
Scott D. Thayer
9. PERFORMING ORG'XNIZATION 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 parameters of ground traffic
behavior in and around airports, including trip generation rates and vehicle
running time, to more readily available characteristics of airports, including
size and nature of airport population and size and nature of air traffic. Such
relationships are to be used to relate airport characteristics to air quality.
7.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Air Pollution, Airports, Urban Planning
Traffic Engineering, Transportation Manage-
ment, Transportation Models, Land Use,
Regional Planning, Urban Development, Urban
transportation, Vehicular Traffic, Highway
'Tanning
Indirect Sources
Indirect Source Review
13 B
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