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introduction and Overview
THE PUBLIC POLICY CONTEXT
There are strong indications, from the decennial Census of Population journey-to-
work statistics and from the periodic Nationwide Personal Transportation Surveys
(NPTS), that the average occupancy of private vehicles used for commute trips
has been falling since the 1960s. For example, for 31 of the nation's most
populous metropolitan areas, the private vehicle occupancy for trips to work
averaged 1.16 persons per vehicle in 1970; by 1990, this statistic had fallen to
i.09, a drop of 6.6%.~
More markedly, for the country as a whole the proportion of work trips made by
driving alone in a private vehicle increased from 64.4% of all commuters in 1980
to 73.2% by 1990. This growth - implying over 22 million extra vehicles on the
nation's roads, many of them during peak periods - came primarily at the expense
of ridesharing in private vehicles, and to a lesser degree from transit riders.
The switch from ridesharing to drive alone was more marked during the 19SOs
than it had been in the 1970s. By 1990, the proportion of private vehicle
commuters who were riding in vehicles with four or more travelers was roughly
I% nationally, down from about i.7% in 1980.
What has happened to carpooling?
Given that the large and continued increases in the proportion of commuters
driving alone during the 1980's are mostly attributable to the decline in
carpooling, the question would seem to be, "Why doesn't anyone carpoo} as much
as they used to do?" Perhaps the higher occupancies we observed in two previous
decades reflected a passing "fad," spurred on by a sudden awareness of limited
energy resources, high gasoline pnces, and the growing interest in the
environment. But on closer inspection, there appear to be some important
structural changes taking place in the ways we live and work that may go a lot
further in explaining the decline.
Of the 31 metropolitan areas, only ten had constant geographical boundaries over the twenty
years. The comparisons above make some adjustment for boundary changes, but if attention is
restricted solely to the ten unchanged metropolitan areas the decline in average occupancy is
somewhat less, about 3.9%.
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Workplace and Residential Geography
Overall population growth in the United States has been quite slow in recent
years, averaging less than i% per year since 1980. in fact, with the exception of
the great depression of the 1930s, the rate of population growth during the 1980s
was the slowest in US history. But this national figure masks some other very
important trends. While the total growth in population is at a historic low, it is at
the same time becoming increasingly aggregated around large urban centers.
Nearly all of the increase in population during the 1980s, about 22 million people,
occurred in metropolitan areas.2 The percent of the total population living in
metropolitan areas has increased from less than 70% in 1970 to 80% in 1994 (the
most recent year for which data are available), and over half of all Americans now
live in metropolitan areas with a population of one million or more.3
The trend within metropolitan areas is even more instructive. Figure 2 shows that
nearly all of the population growth that has occurred in the post-war penod has
been in the suburbs, and that trend continued through the 1980s such that now
nearly half of the US population (47%) lives in suburbs, up from 43% in 1980 and
more than double the 1950 share of 23%.4 Moreover, this growth has also been
fueled by internal migration of metropolitan area residents-central cities lost
over two million residents per year to the suburbs during the 1980s.5
250
-
~n
o 200
-
._
-
o
A_
o
150
100
50
a
Figure 1. US Population by Major Geographic Area 1950-1990
300
: _
.
:::::::::::::::::::::::::
......
,..~......................................
W ..... .........
~ ; ..
.....................................
......
..................
Suburban population
Central city population
1950 1960 1970 1980 1990
Source: Pisarski, p. 19, using data from the 1990 Census.
2
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Not surprisingly, the pattern of job growth has paralleled that of population, as
illustrated in Figure 2. Nearly two thirds of the job growth during the 19SOs
occurred in the suburbs, such that 42% of all jobs are now located there.6
Figure 2. Job Distribution by Major Geographic Area
Growth in Jobs by Geographic Area 1980-1990
Source: Pisarski, p. 26, using data from 1990 Census.
Suburbs
42%
\....'.~.2...:...'.'''"'"''""""'"'''"'"""""''""."'''''"'' - ,
Nonmetro ~ : _
12O/^ ~ ~
~ i, ~ . ,, . ~
V..'..~....'....2..._
... ............ _
............ , _
......
I_
1~
, On metro
20%
Distribution of Jobs by Geographic Area 1990
As people have increasingly chosen to live in metropolitan areas, the pressure for
these areas to expand outward has intensified. At the same time, migration within
metropolitan areas of population and jobs away from central cities and into the
suburbs has further exacerbated this trend. The result has been the continued
dispersion of homes and workplaces and the rise of the suburb as the dominant
pattern of development in the American geography. More disparate residential
and workplace locations make carpooling more difficult, and may be a key driver
in the sharp decline in ridesharing observed during the 19SOs, and the consequent
rise in SOV share.
The Role of Demographics
Surely the concentration of population on one end of the work trip and jobs on the
other make the opportunities for carpooling more numerous, other things being
equal. But there may be other trends at work at an even finer level of geography
that may also influence these opportunities.
The most recent data from the NETS reveal that carpooling is most likely to occur
among people living in the same household. For example, over 62% of two
person carpools were formed with other household members in 1990, and this
proportion has increased from 50% in 1983 (perhaps another result of the
3
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dispersion of residences and jobs described above ).7 It would follow logically
that the number of persons per household, or (more precisely) the number of
workers per household, would be a factor in the rates of carpooling and SOV use
we presently observe.
Many have observed the changes in household and family structures that have
been occurring in recent decades. Indicative of this trend is the fact that the
proportion of the population that is either divorced or has never married, when
adjusted for changes in the age distribution, has risen from 17.5% in 1970 to
31.~% in 1995.8 This and other socioeconomic and demographic changes have
combined to shrink average household sizes markedly since 1960, as shown in
Table 1. With fewer people per household, opportunities for carpooling are
further reduced.
Table 1. US Household Composition 1960-1990
~ ~ ~ - ~ ~ ~ ~ . ~ ~ At. ~ ~ ~ . ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ . ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ . ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ . . . ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ !? ~ . ~ ~
Year I- ~ ~ ~ Percent Oine ~ ~ ~ ~ ~ Mean ~ ~- Mean
: : ~ ~ ~ ~ ~ ~ Person ~ ~ ~ ~ Persons per ~ ~ Workers per
Households I- Householcl ~ ~ Household ~
1960 1 13.1% 3.33 1 1.22
1970 17.1% 3.14 1.21
1980 22.7% 2.76 1.20
1990 1 24.6% 2.63 | 1.25
Source: US Bureau of the Census, 1996.
Socioeconomic Factors
According to the Census Bureau, disposable personal income per capita rose
almost 20% in real terms between 19SO and 1990,9 while real average household
incomes increased 12%.~° At the same time, Figure 3 shows that the cost of new
cars has actually been declining slightly when adjusted for quality. Even more
striking though, is the drop in real gasoline prices that has occurred since 1980,
and the fact that they are now at an all time low, as shown in Figure 4. These
developments have made automobile ownership and use easier than ever, and it
has continued to increase accordingly as shown in Table 2.~
Increased dispersion should make it more difficult to find a neighbor or co-worker to carpool
with. To the extent that this may reduce the absolute number of. non-household carpools, other
things being equal this would cause household-based carpools to increase as a percent of the total.
4
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Figure 3. Measures of New Car Prices 1968 -1993 (thousands of dollars)
25
20
15
10
O ~
68 70 75 80 85 90
-
r
Year
Average market
price per vehicle
Market price of
constant-quality
vehicle
Source: DRI/McGraw Hill, 1994.
CPI-adjusted
price of
constant-quality
vehicle
Figure 4. Real Average Price of Gasoline in the US 1920-1993
160
~ 150
O 140
cn 1 30
Cal
~ 120
-
O 110
Cal
C) 100
90
C: 80
70
~ :~
: ~ ~
1920 1930 1940 1950 1960 1970 1980 1990
Source: Oil & Gas Journal Energy Database.
5
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Table 2. Vehicle Availability 1960-1990
:~ Year ~ ~ ~: ~Vehicles per ~Vehic es
: Capital I: ~Household
1960 0.31 1.03
1970 0.39 1 .25
1980 0.57 1 .61
1990 0.61 1 .66
. .. . . .. . . . .. . ..
Source: Federal Highway Administration.
While the dispersion of workplaces and residences, combined with shrinking
household sizes, will reduce the opportunities to carpool, more available vehicles
that are also cheaper to own and use will tend to reduce the need to carpool, even
among those who have good ridesharing alternatives to driving alone.
The Decline in Transit Shares
As we have seen, much of the increase in SOV shares observed in the last twenty
years came at the expense of carpooling, but the remainder came largely from
eroding public transit shares. It would appear that many of the same factors at
work in the decline of carpooling may also be involved in the reduction of public
transit use in the US.
Surely having more household vehicles available might lessen the need to take
transit, or at least provide an alternative. Moreover, this alternative has become
all the more competitive with the drastic fall in real gasoline prices since 1980,
and the fact that cars are now better made and more comfortable than ever, while
at the same time cheaper to own. Likewise, even absent these developments
n sing incomes have made them easier to afford.
But perhaps the factor that the drop in carpooling and the loss of transit ndership
have most in common is geography. Because transit services must be provided
over a fixed network, they rely heavily on a concentrated base of patrons in order
to provide services that are competitive while efficient.
It is reasonable to assume that people who can afford to use a private vehicle will want to do so.
Private vehicles have certain service qualities that no form of public transit can match and that
people value very highly, such as privacy, route flexibility, and infinite departure schedule
variability.
6
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Transit use and the density of development
We can certainly observe this apparent truism in that the larger and most densely
developed metropolitan areas tend to be the ones with the highest transit shares.
Table 3, taken from our report for TCRP project H-4A,~2 summarizes the use of
transit for travel to work in the 39 metropolitan areas whose 1990 populations
exceeded one million, and shows that between them, these metropolitan areas
account for seven out of every eight of the nation's transit tnps.
Table 3. Transit share of work trips in major metropolitan areas, 1990
Population
density
(persons/sq. ml.)
Transit share
to work
(onto)
New York City2,446 26.9
4 MSAs with population 3+ mn.,
density 1,000+ per sq. ml.1,238 10.5
6 MSAs with population 3+ mn.,
density < 1,000 per sq. ml.573 5.8
10 MSAs with population 2 to 3 mn.522 4.6
18 MSAs with population 1 to 2 mn.470 3.3
A1139 MSAs with population 1+ mn.664 8.7
Source: Charles River Associates (1997~.
New York City is always an extreme outlier in any discussion of the national
transit picture. While completely atypical of the situation throughout the rest of
the country, it is nevertheless an extremely important outlier. New York alone
.. , . .. . . as.
accounts for over one-third of the nation's transit trips. The population density in
the New York metropolitan area is over 70% greater than that in the next most
dense metropolis (Chicago), and transit's share of trips to work in New York
(27%) is about twice as high as for the nearest contenders (Chicago and
Washington DC).
Four other metropolitan areas had 1990 populations of over three million and
residential densities exceeding :1,000 people per square mile: Chicago, Boston,
Philadelphia, and Miami. With the exception of Miami, transit's share of work
trips exceeded 10% in each of these cities.
7
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The importance of core area t rave!
We can extend this argument further by noting that the primary market for transit
services, in cities of any size, tends to be the residents of the central city of the
metropolitan area, traveling to and from jobs also located in the central city. This
is a logical result, given that other things being equal, it is there that we expect to
find the highest concentrations of people and jobs.
The importance of central city trips to transit is still very apparent, although the
story is necessarily a more complicated one. The statistics in Table 4 show that in
1990 about three-quarters of all transit trips made to work by metropolitan area
residents were made to central city workplaces, and that three out of every five
transit work trips were ascribable to central city residents commuting to central
city jobs.
It can also be seen from the exhibit that the two geographical markets of greatest
importance to transit-intra-central city commutes and suburb-to-central-city
commutes are the ones showing the least proportional growth over the 198Os,
6.2% and 9.6% respectively. Moreover, transit's market share fell most heavily
(by almost a third of the 1980 level) for the suburb-to-central-city work trips, and
the Toss in market share for the intra-central city commutes, while proportionately
much less, was still quite marked. As a result, the number of intra-central city
transit work trips fell by almost 13% (or 475,000 trips per day), and from the
suburbs to the central city the decline was over a quarter of all 1980 trips (or a loss
of about 300,000 trips per day).
These results suggest quite strongly that the dispersion of population and jobs
away from the central city described above may be a primary factor in the decline
in transit use. Given that transit depends heavily on the concentration of people
and jobs made possible by the dense core of an urban region, its fixed service
networks are ill-equipped to serve the increasingly diffuse geography we currently
observe.
8
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Table 4. Transit market share for work trips by metropolitan area residents, 1990
and 1980
1990 1 980
Change,
1980 to 1990
Commute trips by all modes (millions)
central citytocentral city24.322.9+6.2%
suburbsto central city15.313.9+9.6%
suburbs to outside MSA6.83.9+72.3%
suburbs to suburbs35.427.7+27.5%
centralcityto suburbs6.04.6+29.8%
centralcityto outside MSA1.91.3+48.1%
all commute trips by MSA residents89.674.4+20.5%
Transit share of commute trips (DO)
central city to central city13.216.1-18.0%
suburbs to central city5.38.0-33.2%
suburbs to outside MSA6.47.6-15.4%
suburbs to suburbs1.21.6-28.0%
central city to suburbs5.15.6-8.5%
central city to outside MSA8.37.3+13.7%
al/ commute trips by MSA residents6.07.9-24.9%
Distribution of transit commute trips by geography (TO)
central city to central city60.262.5
suburbs to central city15.018.9
suburbs to outside MSA3.15.1
suburbs to suburbs8.07.5
central city to suburbs5.74.4
central city to outside MSA3.01.6
all commute trips by MSA residents100.0100.0
Source: Charles River Associates (1997~.
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Implications of These Trencis
The trend in recent years has been for population and employment to become
more concentrated in metropolitan areas, but at the same time it has aggregated in
diffuse suburbs rather than dense central city areas. The vastly larger array of
origins and destinations that must now be served for commuting trips makes both
carpooling and transit use far less feasible, and the statistics marking their decline
reflect this.
A continued decline in ridesharing and transit use, particularly for peak period
commute trips in major metropolitan areas, would obviously place increased
strain on the nation's urban transportation system. Per traveler or per passenger-
m~le, single-occupant vehicles consume more road space and fuel, and result in
greater noxious emissions, than any other mode of local transportation.
As the above discussion makes clear, modal shares and private vehicle occupancy
levels are influenced, over time and across localities, by a wide variety of primary
and intermediate factors: demographic/socioeconomic variations, private vehicle
availability, development patterns, transportation facilities and services, pricing,
and so on. Many of these factors are largely outside the direct control of public
agencies, but others may be influenced - directly or indirectly - by public
investment, financing, pricing, regulatory, or promotional policies.
PROJECT OBJECTIVES
The primary goal of this work is to gain a stronger quantitative understanding of
the factors that have been affecting the dynamics of commuter mode choices and
vehicle occupancy levels in recent years, and to use this improved understanding
to comment on the potential efficacy of policies designed to increase transit
ridership and/or to increase average vehicle occupancy levels.
The potential impacts of continuation of the trends in single occupant vehicle
(SOY) use are numerous. They include both relatively direct, near-term effects, as
well as consequences that would be realized only over a period of many years.
The short-term effects include such relatively easy-to-observe factors as energy
consumption, moving source emissions levels, and the patterns of use of
transportation facilities. The longer-term effects, however, may include such
macro-level trends as physical development patterns and metropolitan area
dispersion, regional and national economic clevelopment, air quality, and so on.
10
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While such phenomena are plausibly linked with travel conditions and behavior,
the causal relationships are not well understood.
It's just not clear, for example, how far transportation (either private or public)
drives dispersion of homes and jobs, or how far the causality flows in the opposite
direction. The marketplace evidence appears to indicate that, at least under the
public regulatory and financial ground rules that have prevailed throughout the
twentieth century, the general public has aspired very strongly to both lower
density living and increased automobility. Their decisions about where to locate
residences and businesses are influenced by a myriad of factors. Transportation
considerations are traded off simultaneously against development densities,
property values, the quality of educational and other public services, social
linkages, macroeconomic factors, and so on. Disentangling credible causal
relationships about the minutiae of land use and transportation interactions is a
formidable undertaking, and perhaps an ultimately impossible one.
Similarly, the causal links between local air quality and vehicular use patterns is
an almost equally difficult analytical problem. It is true, of course, that there are
extant quantitative lance use and air quality models that try to capture at least the
key ways in which urban form and air pollution are correlated with transportation
supply and demand characteristics, given existing (or past) public financing and
regulatory postures. The deficiencies of these models are well-recognized, at least
by the analysts if not fully by the public policy process that desperately needs
some quantitative basis, however deficient, for setting and enforcing standards,
promoting policies, forecasting outcomes, and evaluating program performance.
So, identifying the more direct, near-term potential impacts of continued SOV
growth is inherently more tractable than trying to predict such lonaer-run
· ~ ~ . ~ te ~ ~ . ~ ~ ~ ~ ({ · . ~
implications as ~ developmenVurban 0,77 ~ environmental quality," "economic
productivity," or "social effects," all of which are influenced by very many forces
as well as transportation. In deciding how best to focus the project resources,
therefore, tractability was the chief criterion. In addition, we also considered two
other important factors:
· Overlap with other, more specialize`] TCRP studies.
The relationship between transportation considerations and metropolitan area
development was considered in much more detail in Project H-l, An
Evaluation of the Relationships Between Transit and Urban Form, and is also
the subject of current Project H-iO, The Costs of Sprawl - Revisited. Some of
the social impacts of transportation policy are being explored in Project H-S,
11
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Using Public Transportation to Reduce the Economic, Social, and Human
Costs of Personal Immobility.
Relevance and complementarily to the previous Project H-4A work.
Project M-4A, to which this effort was an adjunct, was concerned with choice
of urban travel mode, and in particular, with the influence of various public
policies on travel choices. it was therefore intended that this new work add to
the understanding of the mode choice process developed in the previous
project.
For reasons like these, we chose to concentrate on the most direct, near-term
implications of the trends in mode choice and private vehicle occupancy levels. in
order to be most relevant to the transit community, we also wanted to focus
attention on those market situations in which transit has traditionally been
regarded as a viable, cost-effective alternative to lower-occupancy modes,
primarily commute trips within central areas or between the suburbs and central
areas. Most of the existing discussion of trends in commuting mode shares has
been primarily descriptive in nature, as distinct from analytical.
Therefore, the specific objectives of this project are:
.
LITERATURE REVIEW
To explore credible, quantitative causal relationships concerning the
variations and trends in mode shares and in vehicle occupancy levels,
particularly in those market situations in which transit has traditionally been
considered most competitive with private transportation, using multivariate
analysis techniques;
To use the identified quantitative relationships to assess the extent to which
these observed trends can be explained by factors within the control of
transportation policymakers; and
To comment on the implications of our findings for potential public policies
designed to favor higher occupancy modes (such as road pricing proposals).
The societal and technical implications of increasing reliance on private
transportation has been examined before, in much larger studies than this one.
One such effort that comes to mind was cauied out in the late 1970s by the
Congressional Of flee of Technology Assessment. ~ 3 While some of the
12
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assumptions and discussion in that report now inevitably appear dated, the study
did a strong, even-handec! job in addressing both the negative and positive impacts
of continued growth in automotive travel, and the many of the major conclusions
still appear to be valid. The study accurately predicted, for example, that auto
travel would increase markedly through the 19SOs and 199Os despite stricter fuel
economy standards and incentives to reduce pollution, and that as a consequence,
air quality standards would be increasingly violated and urban mobility would be
hampered by increased congestion levels.
There have been several other large studies on the effects of auto ownership and
urban form on commuting mode choices. A 1976 study by Kain and Fauth of
Harvard University developed a large number of models of automobile ownership
and use based on a household sample from the 125 largest metropolitan areas in
1970.~4 These models used as explanatory variables measures such as highway
capacity, transit service levels, housing density, and socioeconomic factors such
as household income. In a report of the same year, Ben-Akiva and Lerman
developed disaggregate models of auto ownership and mode choice from
Washington DC data for 1968.~s This study tested the hypothesis that auto
ownership and mode choice decisions are interdependent, and are therefore made
jointly. The resulting models confirmed this hypothesis, and related auto
ownership and use to auto and transit service levels and costs, as well as
socioeconomic land use/urban structure variables.
A 1981 study by Zahavi, Beckmann, and Golob examined the simultaneous
process of housing location choice and mode choice through the development of
"travel probability fields", mode] parameters which allow travel patterns to be
related to urban structure in a dynamic feedback process.
Some of the best-known work in this area, and perhaps the work most closely
resembling the present study, is that by the Australian authors Newman and
Kenworthy. Their 1989 book Cities and Automobile Dependence: A Sourcebook,
analyzes the relationship between auto use and variety of land use and other
factors across a series of major cities throughout the worId.~6 This analysis is
used to develop a city typology with respect to "automobile dependence", which is
then used to classify each of the cities considered in the study. The book also
provides a thorough compendium of statistics for each city, as well as an excellent
bibliography on the subject.
A little more recently, Prevedouros en c! Schofer analyzed auto ownership and use
in two low-density, outer ring growing suburbs and two high-density, inner-ring
stable suburbs.~7 This 1992 paper found the stage in the lifecycle to be a major
13
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determinant of automobile ownership, and that the number of workers, people of
driving age, and suburban residence locations are also positive factors. Transit
use was found to influence auto ownership negatively, as was the location of
workplaces in the central city. The availability of company cars was found to be a
positive influence on automobile use.
A 1996 paper by Schimek reexamined the issue of residential density and its
effect on automobile use. This analysis estimates a mode} of vehicle travel that
includes vehicle ownership as an intermediate factor and assumes travel and
density are determined simultaneously. The author finds that density has a
negative effect on auto travel, but that this effect is quite small, and smaller than
the influence of income.
On the subject of journey to work trends in mode choice with which this study is
primarily concerned, there have been several important national-level analyses.
The best known of these is Alan Pisarski's Commuting in America survey of
commuting patterns, cited earlier. The study was originally carried out in 1987
using data from the 1980 Census, and has recently been updated with new data
from the Census of 1990. These studies examine national trends in commuting
flow patterns, mode choice, demographic and socioeconomic measures. Another
recent synthesis of national commuting trends using data from both the Census
and NETS and also offering some insights into the future of commuting
alternatives was published in 1994 by National Urban Transit Institute at the
University of South Florida.~9 This study provides recommendations for the
future success of public transportation, ridesharing, and working at home.
At a slightly more disaggregate [level, there have been two reports commissioned
by the Federal Highway Administration that examine these trends for the nation's
largest metropolitan areas. The original report, titled Journey-to-Work Trends
Based on the 1960, 1970, and 1980 Decennial Censuses, was also co-authored by
Pisarski and published in 1986.2° The more recent version by Rosetti and
Eversole2~ is an update using data from the 1990 Census, and contains a large
amount of additional data and tabulations.
A series of special reports have also been prepared based on data from the 1990
NPTS. They include volumes on mode of travel,22 demographics,23 and trip and
vehicle attributes.24 Each volume contains several papers on specific topics of
interest, including travel by women and the elderly, the decline in carpooling, and
trip chaining.
14
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Work has recently been cattier out by the Hickling Corporation under funding
from the FTA's Office of Policy, to collect and analyze data from some 17
prototypical, diverse congested corridors in US metropolitan areas.25 This work
attempts to quantify the magnitude of economic inefficiencies represented in
current traffic patterns in those corndors, following a Mogridge/Glaister type of
mode! used to analyze transit infrastructure investments in London and other
cities. In addition, it explores appropriate road pricing policies that would redress
those inefficiencies, and (as a second best solution in the absence of marginal cost
pricing of road space) identifies feasible transit investment and operational
policies that might also lead to net societal benefits in the corridors. Finally, the
results of TCRP Project H-4B, Transit Markets of the Future The Challenge of
Change, provide a detailed analysis of future economic, demographic, social, land
use, and transportation policy trends and the implications of these trends for the
future of public transit in the US.26
THE STRUCTURE OF THIS REPORT
The remainder of this report describes the details of the research and analysis that
was carried out for the project. Chapter 2 provides details on the development of
the databases used in the analyses. The discussion includes a review of relevant
data sources that have been used or are potentially useful in studies of this nature,
and the subsequent compilation of both cross-sectional and time-series data for
use in the analysis.
Chapter 3 presents the main findings from the study, including the results of the
mode} estimation using the datasets descnbed in Chapter 2. Several models are
presented, for several market segments. These results include both cross-sectional
and time-genes analyses. Chapter 4 presents our main conclusions based on these
findings and an analysis of the implications of the results. The conclusion of the
chapter also provides recommendations for further research. Finally, a series of
appendices provide more details about the data used in the study.
NOTES:
Calculations by CRA from data presented in
Rosetti, M.A. and Eversole, B.S., Journey-To-Work Trends in the United States and its Major
Metropolitan Areas, 1960-1990. Washington, DC, US Department of Transportation, Federal
Highway Administration, (1993~.
2 Pisarski, A. E., Commuting in America 11. Lansdowne, VA, Eno Foundation for Transportation,
Inc. (1996), p. 18.
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3 US Bureau of the Census, Statistical Abstract of the United States 1996. Washington, DC,
(1996), Tables 40 and 41.
4 Pisarski, p. 19.
s Ibid.
6 Pisarski, p. 26.
7 Vincent, M.J., Keyes, M.A., et. al., Nationwide Personal Transportation Survey: 1990 NPTS
Urban Travel Patterns. Washington, DC, US Department of Transportation, Federal Highway
Administration, ( 1994), Table 5-7.
~ US Bureau of the Census, op. cit., Table 58.
9 US Bureau of the Census, op. cit., Table 700.
a US Bureau of the Census, Statistical Abstract of the United States 1992, Washington, DC,
(1992), Table 698 and US Bureau of the Census, Statistical Abstract of the United States 1982-83,
Washington, DC, (1983), Table 711, with adjustment to constant dollars using CPI from US
Bureau of the Census, Statistical Abstract of the United States 1996, Washington, DC, (1996),
Table 745.
i~ Rosetti, M.A.and Eversole, B.S., op. cit.
i2 Charles River Associates, "Building Transit Ridership An Exploration of Transit's Market
Share and the Public Policies that Influence It." to be published as TCRP Report H-4A (1997~.
|3 US Congress, Office of Technology Assessment, Technology Assessment of Changes in the
Future Use and Characteristics of the Automobile Transportation System (3 volumes).
Washington, DC, US Government Printing Office (1979~.
{4 Kain, J.F. and Fauth, G.R., The Elects of Urban Structure on Household Auto Ownership
Decisions and Journey to Work Mode Choices. Prepared for the US Department of
Transportation, Cambridge, MA, Harvard University Department of City and Regional Planning
(1976).
Is Ben Akiva, M.E. and Lerman, S.R., A Behavioral Analysis of Automobile Ownership and Mode
of Travel (3 volumes). Washington, DC, US Department of Transportation, (1976~.
i6 Newman, P.W.G. and Kenworthy, J.R., Cities and Automobile Dependence: A Sourcebook.
Sydney, Australia, Gower Technical Press (1989~.
|7 Prevedourous, P.D. and Schofer, J.L., "Factors Affecting Automobile Ownership and Use."
Transportation Research Record 1364, pp. 152-160.
~8 Schimek, P., "Household Motor Vehicle Ownership and Use: How Much Does Residential
Density Matter?" Transportation Research Record 1552, pp. 120-125.
~9 Ball, W.L., Commuting Trends in the United States: Recent Trends and a Look to the Future.
Prepared for the US Department of Transportation, Tampa, FL, Center for Urban Transportation
Research, (1994~.
20 Briggs, D., Pisarski, A., and McDonnel, J., Journey-to-Work Trends Based on the 1960, 1970,
and 1980 Decennial Censuses. Washington, DC, Federal Highway Administration, (1986~.
2} Rosetti, M.A.and Eversole, B.S., op. cit.
22 Nationwide Personal Transportation Survey: 1990 NPTS Travel Mode Special Reports.
Washington, DC, US Department of Transportation, Federal Highway Administration, (1994~.
23 Nationwide Personal Transportation Survey: 1990 NPTS Demographic Special Reports.
Washington, DC, US Department of Transportation, Federal Highway Administration, (1995~.
24 Nationwide Personal Transportation Survey: 1990 NPTS Special Reports on Trip and Vehicle
Attributes. Washington, DC, US Department of Transportation, Federal Highway Administration,
(19951.
25 The Role of Transit in Congestion Management. Hickling Lewis Brod Inc., prepared for the
Federal Transit Administration, ( 19961.
16
OCR for page 17
26 The Drachman Institute for Land and Regional Development Studies, "Transit Markets of the
Future The Challenge of Change." To be published as TCRP Report H-4B (1997), Washington,
DC, Transportation Research Board.
17
Representative terms from entire chapter:
commute trips
