CSIS Discussion Paper No. 69

Spatio-Temporal Dimensions of Commuting Inequality Between
Car and Public Transit: The Case of San Francisco

byMizuki Kawabataa* and Qing Shenb

March, 2006a*Corresponding author. Center for Spatial Information Science, University of Tokyo
5-1-5 Kashiwano-ha Kashiwa-shi Chiba, 277-8568, Japan
Email: mizuki@csis.u-tokyo.ac.jpbSchool of Architecture, Planning, and Preservation, University of MarylandCollege Park, MD 20742, USA
Email: qshen@umd.edu

Abstract
The issue of equity is increasingly recognized as an essential component of sustainable
development and transportation. Utilizing 1990 and 2000 spatial data and GIS, we
conduct a spatial and temporal examination of commuting inequality between car andpublic transit in the San Francisco Bay Area. Results visualized in the maps show
considerable inequality in job accessibility and commuting time between car and public
transit and among locations within the metropolitan area. The visualized results also show
substantial spatial variations in the temporal changes in job accessibility and commuting
time for car as well as for public transit. Results from OLS and spatial regression models
indicate that in both 1990 and 2000 greater job accessibility is significantly associatedwith shorter commuting time for driving alone as well as for public transit, but the degreeof this association is considerably greater for public transit than for driving alone.

1

Introduction

The issue of equity is increasingly recognized as an essential component of sustainable
development and transportation (Banister, 2002; Deakin, 2001, 2002; FHWA, 2001;
Johnston and Garry, 2003; Richardson, 2005; Steg and Gifford, 2005). Researchers andpolicymakers have come to agree worldwide with the notion that sprawling, auto-oriented
urban development is unsustainable, where a critical issue is inequality in job access andcommuting to work between car and public transit users. In geographically dispersed,
low-density US metropolitan areas, for example, the number of accessible job
opportunities, referred to hereafter as job accessibility, is considerably lower for public
transit users than for car users (Hess, 2005; Kawabata, 2003b; Kawabata and Shen, 2006;
Shen 1998). A growing body of research in fact suggests that in highly auto-oriented
metropolitan areas, lack of access to a car is a barrier to participation in economic
activities (Blumenberg, 2004; Cervero et al., 2002; Gurley and Bruce, 2005; Kawabata,
2003a; Sanchez, 1999; Taylor and Ong, 1995). It is also reported that people who use
public transit spend much more time commuting than people who use cars (Kasarda,
1995; Taylor and Ong, 1995).
Under these circumstances, however, a limited number of studies have so far
focused on the spatial and temporal dimensions of commuting inequality between car andpublic transit. Much is known about the presence of substantial spatial variations incommuting time within a metropolitan area (Shen, 2000), but little is known about thepotential presence of spatial variations in commuting time when the two travel modes, carand public transit, are differentiated. Given the considerable car/public transit disparity in
job accessibility, one might suspect that location is a more important factor associatedwith commuting time for people who use public transit than for people who use cars.
Nevertheless, the extent to which the association between urban spatial structure andcommuting time differs between car and public transit is not yet fully understood. Neither
the existence of temporal changes in this association nor the differences in changes, if any,
between car and public transit has been sufficiently investigated. The spatial and temporaldimensions of commuting by car vis-à-vis public transit are a relatively under-researched1

area partly because data on commuting time sorted by travel mode for small geographic
areas are not readily available.
The recent advances in and simplification of spatial data and Geographic
Information Systems (GIS) have allowed us to further explore this research area. Thus, bymaking use of 1990 and 2000 spatial data and GIS, we examine the spatial and temporaldimensions of commuting inequality between car and public transit. The study area is the
San Francisco Bay Area, which has a relatively high level of public transit usage with aslight increase in the proportion of public transit commuters occurring in the 1990s; thiswas the first such increase since 1960.
We specifically consider the relevant situations mentioned above and examine
car/public transit inequality with the following two lines of questions. The first line ofquestion is whether there are substantial spatial and temporal variations in job
accessibility and commuting time within the metropolitan area. To answer this question,
we calculate and visualize job accessibility and commuting time for car and public transit
users separately. In measuring job accessibility, our study takes into account spatial
variations in the supply and demand sides of the labor market (i.e., jobs and workers) as
well as the distinction between the travel modes (i.e., car and public transit), which haverarely been simultaneously addressed. The second line of questions are whether the
association between job accessibility and commuting time is greater for public transit
than for driving alone and also whether this association changed between 1990 and 2000.
To answer these questions, we examine ordinary least squares (OLS) and also two typicalspatial regression models which take into account spatial autocorrelation, spatial lag andspatial error models. We address the spatial regression models since the presence ofconsiderable spatial autocorrelation is suggested by regression diagnostics. The
application of spatial regression models is rarely conducted in this area of research andtherefore is a unique methodological feature in this study. It has to be noted that the
subject of this study is not to identify causal relationships between job accessibility andcommuting time but to examine car/public transit differences in the association between
job accessibility and commuting time. Here, the spatial unit of the analyses is the regional
travel analysis zone (RTAZ), a rather disaggregated area serving the purpose of thisresearch.
The remainder of this article starts with a review of related research in Section 2.
We subsequently describe the study area and methodology in Section 3 and Section 4,
respectively. Results are then presented in Section 5, and conclusions are given in Section
6.

2

2

Prior Research

2.1

Job Access and Transportation

Geographical separation between workplace and residence, often called spatial mismatchin academic literature, has garnered considerable attention since Kain (1968) introduced it
as a possible explanation for concentrated urban poverty. Kain hypothesized that
residential segregation and employment suburbanization deteriorate minority workers’
job opportunities and aggravate their employment outcomes. Since Kain’s study, aconsiderable number of studies have been conducted to examine spatial mismatch, andscholars provide summaries and reviews (Blumenberg and Manville, 2004; Glaeser et al.,
2004; Holzer, 1991; Ihlanfeldt and Sjoquist, 1998; Kain, 1992).
While Kain’s original spatial mismatch theory does not address the issue oftransportation, an increasing number of subsequent studies recognize the importance oftravel modes in accessing employment, especially on the part of disadvantaged people
such as low-skilled workers and welfare recipients. Some studies, for example, find thathaving access to a car significantly facilitates the transition of welfare to work (Cervero etal., 2002; Gurley and Bruce, 2005; Ong, 1996, 2002). The positive effect of car
ownership on employment outcomes is also found for the more general population(Raphael and Rice, 2002). Results for the relationship between public transit access andemployment are rather mixed, however. A study using 1990 census data in Atlanta andPortland finds that access to public transit increases nonwhite workers’ labor participation(Sanchez, 1999). Another study using data on welfare recipients in six US metropolitan
areas (Atlanta, Baltimore, Dallas, Denver, Milwaukee, and Portland), on the other hand,finds that transit access/service quality has no significant effect on improvement in
employment status (Sanchez et al., 2004).
In analyzing problems related to job access, a certain index of job accessibility isoften used. Accessibility is measured in various ways depending on the purpose of
research (Handy and Niemeier, 1997; Harris, 2001; Morris et al., 1979). A popularmeasurement of job accessibility uses the potential-based approach, which calculates thenumber of job opportunities available depending on given travel costs such as travel
distance and time. The potential-based measurement of job accessibility has beendeveloped in various forms, especially since Hansen’s (1959) study on accessibility andland use in which accessibility is defined as the potential of opportunities for interaction.
In the case where spatial variation and travel-mode inequality in job access are ofinterest, two issues arise when measuring job accessibility. One issue is the joint
incorporation of the supply and demand sides of the labor market. In many cases job
accessibility is computed by incorporating the supply side (jobs) only, and the demandside (workers who compete for those jobs) is not factored in. Since jobs and workers are
not uniformly distributed within a metropolitan area, job accessibility without considering

3the spatial distribution of workers (i.e., spatial competition) can generate a distorted
picture (Harris, 2001; Shen, 1998; Van Wee et al., 2001; Weibull, 1976).
Another issue is the differentiation of travel modes. Job accessibility is in many
instances calculated for all travel modes or only for car travel. It is known, however, that
commuting time by public transit is much longer on average than commuting time by car(Kasarda, 1995; Taylor and Ong, 1995). Also, there is growing evidence that job
accessibility for public transit users is markedly lower than that for car users. It is found,for example, that the ratios of the number of low-wage jobs accessible within 30 minutes
by car to the number of jobs accessible by public transit are considerably high in lowincome neighborhoods in Los Angeles (Blumenberg, 2004) and Erie and Niagara
Counties in western New York State (Hess, 2005). When the focus is on inequality in job
access between car and public transit, therefore, it is vital to differentiate between thetravel modes. The differentiation of travel modes is especially important for highly autooriented US areas, since job accessibility for public transit in the US is extremely low
compared to more transit-oriented areas--for instance, Tokyo (Kawabata and Shen, 2006)
and Hong Kong (Kwok and Yeh, 2004).
While a number of studies suggest considerable disparity in job access between
car and public transit in the US, the car/public disparity in recent years and its temporalchanges are not yet fully explored.

2.2

Urban Spatial Structure and Commuting Time

Relationships between urban spatial structure and travel patterns or behavior have been
the subject of extensive research (reviews are given in, for example, Badoe and Miller,
2000; Boarnet and Crane, 2001; Cervero, 2002; Crane, 2000; Ewing and Cervero, 2001;
Horner, 2004; Rouwendal and Nijkamp, 2004). Commuting patterns and their relation tourban spatial structure or land use have drawn interest as concerns about traffic
congestion and environmental burdens have grown. In particular, the relationshipbetween job and workers’ housing locations and journeys to work have been actively
examined and debated. One body of research measures wasteful commuting or excess
commuting which represents the difference between observed and theoretical minimalcommuting given real distributions of jobs and housing (Giuliano and Small, 1993;
Hamilton, 1982; Merriman et al., 1995; Small and Song, 1992; White, 1988). Anotherbody of research examines the relationship between jobs-housing balance and commuting.
Some researchers suggest significant relationships between jobs-housing balance andtravel patterns (Cervero, 1989; Nowlan and Stewart, 1991). Other researchers, on theother hand, question the relationships and the efficacy of jobs-housing balancing policy in
reducing congestion (Giuliano, 1991; Giuliano and Small, 1993; Peng, 1997; Wachs et al.,1993).
The linkages between urban spatial structure and commuting continue to attract
further investigations as urban spatial structure and travel patterns evolve over time. In
particular, commuting time trends over the past few decades highlight the necessity for4research using up-to-date and temporal data. Studies in the early 1990s indicate that in theUS, commuting times are shrinking (Gordon et al., 1991), largely unchanged (Levinson
and Kumar, 1994; Taylor and Ong, 1995; Wachs et al., 1993), or increasing at a modest
pace (Rosetti and Eversole, 1993). The US Census 2000 data, however, indicate thatduring the 1990s commuting time increased in every one of the 49 metropolitan areas
with populations of one million or more (McGuckin and Srinivasan, 2003). As an
explanation of declining or steady commuting times despite increasing commuting
distance between 1968 and 1988 for the Washington metropolitan area, Levinson andKumar (1994) propose the rational locator hypothesis that workers adjust their job andhousing locations to maintain constant commuting times. The study suggests thatpolycentric and suburbanized urban structure is a result of rational relocations, and thatpolicies encouraging such rational decisions are needed. A recent study by Levinson andWu (2005), however, finds that while drive alone commuting times were stable formetropolitan Washington, DC, commuting times rose for an intra-metropolitan area, theTwin Cities. Average commuting time for an entire metropolitan area may indeed conceal
intra-metropolitan variations in commuting time. A study using 1990 data at the census
block group level for the 20 largest US metropolitan areas shows considerable spatial
variations in commuting time (Shen, 2000). The study subsequently conducts regression
analysis for the Boston metropolitan area and finds significant associations between
urban spatial structure and average commuting time.
A number of studies examine the relationships between urban spatial structure
and commuting durations, and some recent studies take a step to examine their temporalchanges (Horner, 2006; Vandersmissen et al., 2003; Yang, forthcoming). Only a limited
number, however, differentiate between car and public transit in their analyses. Using
metropolitan-area level data for 82 US metropolitan areas in 2000, Gordon et al. (1989)
examine the effects of densities, urban size, and some other urban structure variables on
average commuting times by car and public transit, and obtain similar results for the two
travel modes. Levinson (1998) uses a 1987–1988 household travel survey in metropolitan
Washington, DC, and analyzes the effect of job accessibility and housing accessibility at
traffic zones at home and work destinations on commuting times for car and public transit.
The result indicates that while residences with higher job accessibility are associated withshorter commuting time for car as well as for public transit, workplaces with higher job
accessibility are associated with longer commuting time for car but with shorter
commuting time for public transit. Little is known, however, about the temporal changes
in the association between urban spatial structure and commuting time for car vis-à-vis
public transit.

3

The Study Area

The study area is the San Francisco Bay Area which covers the nine counties of Alameda,
Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, and Sonoma.
Basic descriptive statistics on population and transportation characteristics for the US
nationally (presented as reference) and the San Francisco Bay Area from Censuses 1990
and 2000 are shown in Table 1. In the following, statistics for the San Francisco Bay Area
discussed are otherwise noted.

5

Table 1. National and regional characteristics in 1990 and 2000
United States

Total population (000)Persons 16 years+ in labor force (000)Total households (000)% Households with 1+ vehicles% Households with 2+ vehicles
Means of transportation to work
% Drove alone% Carpooled% Public transportation% Other means% Worked at home
Mean travel time to work (min.)San Francisco Bay Area

1990
248,711125,182
91,994
88.5%
54.7%

2000
281,422
138,821
105,539
89.7%
55.5%

1990
6,024
3,322
2,246
89.5%
57.0%

2000
6,784
3,535
2,466
90.0%
57.0%

73.2%
13.4%
5.3%
5.2%
3.0%

75.7%
12.2%
4.7%
4.1%
3.3%

68.2%
13.0%
9.5%
5.9%
3.4%

68.0%
12.9%
9.7%
5.4%
4.0%

22.4

25.5

25.6

29.4

Drive alone

24.1 *

23.6 **

27.3 **

Public transportation

47.7 *

41.2 **

46.3 **All modes

Note: Public transportation for mean travel time includes taxicab for the US nation but excludes taxicab for the San
Francisco Bay Area. Unmarked data are from 1990 and 2000 Decennial Censuses. *Census Transportation Planning
Package (CTPP) 2000 Profile Sheets (AASHTO, 2002). **Authors' calculations from CTPPs 1990 and 2000.

The population grew between 1990 and 2000. The study area added about
760,000 people to the base of 6 million residents (a 13% increase), 213,000 workers tothe base of 3.3 million workers (a 6% increase), and 220,000 households to the base of
2.2 million households (a 10% increase).
The transportation statistics indicate a high degree of automobile dependence.
Approximately nine out of ten households have one or more cars, and more than half ofhouseholds own two or more cars. A great majority use cars to commute to work. The
proportions of commuters who drive alone and carpool are about 68% and 13%,respectively. The proportion of commuters who use public transit, on the other hand, isconsiderably lower, about 10%, but this figure is twice as high as the US average of 5%.
According to past decennial census data (not shown in Table 1), between 1960
and 1990 the proportion of workers who use cars to commute to work rose from 69.9% to81.2% and the proportion of workers who use public transit fell from 15.4% to 9.5%
(MTC, 2002b). Between 1990 and 2000, however, this trend reversed. Statistics in Table
1 indicate that the proportions of workers who drive alone and carpool decreased slightly,
from 68.2% to 68.0% and from 13.0% to 12.9%, respectively. Combined, the proportionof car commuters decreased from 81.2% to 80.9%. The proportion of public transit
commuters, on the other hand, increased from 9.5% to 9.7%. Indeed, among the 49
largest US metropolitan areas, San Francisco ranked the second in terms of increase in
numbers taking public transit to work (McGuckin and Srinivasan, 2003). For the US
nationally, on the other hand, the trend of increasing auto usage and decreasing public
transit usage continued (but slowed) in the 1990s. During the thirty years between 1960
and 1990, the proportion of car commuters increased from 64.0% to 86.5% and the6

proportion of public transit commuters decreased from 12.1% to 5.3% (McGuckin andSrinivasan, 2003). During the 1990s the proportion of car commuters increased slightly,
from 86.5% (not 86.6% due to rounding) to 87.9%, and the proportion of public transit
commuters continued to decrease from 5.3% to 4.7%.
Travel time to work increased during the 1990-2000 period. The averagecommuting time for the San Francisco Bay Area grew from 25.6 to 29.4 minutes (a 3.8
minute increase). This is a notable increase compared to the increase from 24.3 to 25.6
minutes (a 1.3 minute increase) during the 1980s (MTC, 2002a). The average commuting
time by public transit is considerably longer, about 1.7 times that by driving alone (about
twice for the US nationally). When the data were analyzed separately by travel mode, theaverage commuting time increased for both solo drivers and public transit commuters,from 23.6 to 27.3 minutes and from 41.2 to 46.3 minutes, respectively.

4

Methodology

Methodology comprises two parts. The first part calculates and visualizes job
accessibility and commuting time at the spatial level of RTAZ for car and public transit in1990 and 2000, and examines their spatial and temporal variations and travel mode
disparity. The second part estimates OLS and spatial regression models to examine theassociations between urban spatial structure and commuting time in general and between
job accessibility and commuting time in particular. Results for driving alone and public
transit are then compared. The following three subsections describe in detail themeasurements of job accessibility, regression models, and data, respectively.

4.1

Measurements of Job Accessibility

In order to examine the research questions outlined earlier, we calculate job accessibility
that takes into account the spatial distributions of not only the supply side (jobs) but also
the demand side (workers) as well as the distinction of travel modes (car and public
transit). Job accessibility formulae, which utilize accessibility frameworks developed by
Weibull (1976) and Shen (1998), are shown by :
Ai

car

=

∑

j : t ijcar < t 0

Ai

tran

=

∑

j: tijtran < t0

Ej

∑ α W + ∑ (1 − α )Wkkk : t kjcar < t 0

k

(1)

,

(2)

k

k : t kjtran < t 0

Ej∑ α W + ∑ (1 − α )Wk : tkjcar < t0

,

k

k

k : tkjtran < t0

k

k

7

where Aicar and Aitran are measures of job accessibility in resident zone i for car commutersand public transit commuters, respectively; tcarij and ttranij are the travel times between
zone i and zone j by car and public transit, respectively; t0 indicates a travel time
threshold; Ej represents the number of jobs in zone j; Wk indicates the number of workers(consisting of both the employed and unemployed) living in zone k; αk is the proportionof households with cars in zone k. Note that spatial competition is incorporated directly
into job accessibility. A resultant job accessibility value in resident zone i represents thenumber of jobs within reach for a given commuting time by a particular travel mode for aworker living in zone i.
We assign approximately the average commuting time, 30 minutes, to thethreshold t0. The travel-time threshold approach, rather than the gravity-based approach,is employed to impose the same travel time constraint to car and public transit commuters.

4.2

Regression Analysis

We estimate a set of regression models of average commuting times by driving alone andpublic transit. Our specific questions of interest are: whether the association between job
accessibility and commuting time by public transit is stronger than the association by
driving alone; and whether the association between job accessibility and commuting timechanged from 1990 to 2000. The models are estimated separately for 1990 and 2000. All
models use essentially the same set of explanatory variables, which makes it easier tocompare results. In the models for driving alone, job accessibility for car commuters isincorporated as an explanatory variable, and in the models for public transit, job
accessibility for public transit is included as an explanatory variable. The otherexplanatory variables are neighborhood employment and population densities andsocioeconomic characteristics such as income, gender, race, and occupation compositions(shown later in Table 2), which previous studies suggest are likely to be associated with
commuting time.
Some may question the inclusion of commuting time on the left hand side and a
travel time variable as part of job accessibility measure on the right hand side of theregression models. Commuting time on the left hand side of the models is the averagecommuting time for residents of each zone. The travel time incorporated in job
accessibility on the right hand side of the models, on the other hand, is the average timeto travel from each origin to each destination. This travel time indicates the proximity
between each pair of zones. Using the 30-minute threshold time, a resultant job
accessibility value for a resident zone represents the number of jobs accessible within 30
minutes for a worker residing in that zone, and not commuting time itself.
We start the regression analysis by estimating OLS models that can be expressed, as
usual, by:
y = Xβ + ε ,(3)8where y is a vector (n×1) of observations of the dependent variable; X is a matrix (n×k) of
observations of the independent variables; β is a vector (k×1) of regression coefficients;and ε is a vector (n×1) of error terms.
Regression diagnostics, however, suggested the presence of considerable spatial
autocorrelation. We therefore estimated two basic spatial regression models, spatial lag
and spatial error models, which take into account spatial autocorrelation as described
below.
The spatial lag model incorporates a spatially lagged dependent variable on theright hand side of the regression model as follows:y = ρW y + Xβ + ε ,(4)where W is a spatial weights matrix (n×n); Wy is the corresponding spatially lagged
dependent variable; and ρ is the spatial autoregressive parameter. The spatial weights
matrix, W, is a positive and symmetric matrix which specifies neighborhood sets for eachobservation; wij = 1 when i and j are neighbors, and wij = 0 otherwise. An observation isconventionally assumed not to be a neighbor to itself, so the diagonal elements of theweight matrix are set to zero (wii = 0). In order to make estimated parameters between
alternative models more comparable, the spatial weights matrix is row-standardized sothat the sum of elements in each row is one. Since the dependent variable y at i in a
spatial lag model is correlated with the error terms at all locations in the system, an OLS
estimator will be biased and inconsistent. The spatial lag model, therefore, is generally
estimated by the maximum likelihood or instrumental variable estimation (see Anselin,
1988; Kelejian and Prucha, 1999; Kelejian and Robinson, 1993; Ord, 1975).
The spatial error model, on the other hand, takes into account spatial autocorrelation
by incorporating a spatial autoregressive process in the error terms as follows:y = Xβ + ε ,(5)ε = λWε + ξ ,(6)where λ is the spatial autoregressive coefficient for the error lag Wε; and ξ is an
uncorrelated and homoskedastic error term. The error covariance is nonspherical, and
OLS estimates are unbiased but inefficient. Estimation of the spatial error model isgenerally carried out by the maximum likelihood or generalized moments estimation(Kelejian and Prucha, 1999). In this study, the maximum likelihood estimation is used forboth the spatial lag and spatial error models. The regression models are estimated using
GeoDa, freestanding geodata analysis software (Spatial Analysis Lab, 2005). Moredetailed descriptions of the spatial regression models are provided by Anselin (1988) andAnselin and Bera (1998).
There are a number of ways to specify spatial weights (Anselin, 1988, 2002;
Anselin and Bera, 1998; Cliff and Ord, 1973, 1981). Typical specifications are: the rook-9

(common boundaries), bishop- (common vertices) and queen- (common boundaries aswell as vertices) based contiguity spatial weights; and distance-based spatial weights
where two observations at i and j are considered neighbors if the distance between i and jis less than a given cutoff value. In practice, a spatial weights matrix is rather arbitrarily
selected, especially when there is no formal theoretical foundation for the extent of spatial
interaction. After testing the first-order rook-based spatial weights, queen-based
contiguity spatial weights, and distance-based spatial weights using various cutoff
distance values, we elected to use the first-order queen-based contiguity spatial weights,
as they generally yielded better fits and it is reasonable to consider zones having common
boundaries and vertices as neighbors.
Anselin (2005) suggests that the choice of the spatial lag or spatial error model canbe made based on four Lagrange Multiplier (LM) tests—two LM tests for spatial lag andspatial error dependence (LM-Lag and LM-Error, respectively) and two robust LM tests
for spatial lag and spatial error dependence (Robust LM-Lag and Robust LM-Error,
respectively). Since both LM-Lag and LM-Error test statistics were highly significant inall the estimated OLS models, we subsequently investigated the Robust LM-Lag andRobust LM-Error test statistics. If one of them is more significant than another, the model
for more significant statistics is preferable; if the Robust LM-Lag test statistics is moresignificant than the Robust LM-Error test statistics, the spatial lag model is preferably
estimated, or vice versa. If two of the statistics are highly significant, the model with thelargest test statistics is selected, although in this case we have to be cautious about othersources of misspecification.

4.3

Data

The spatial unit of the analysis is the regional travel analysis zone (RTAZ), which isdelineated to serve as the smallest geographic basis for travel demand model forecasting
systems by the Metropolitan Transportation Commission (MTC), the regionaltransportation planning agency for the San Francisco Bay Area. In this study, we use the1099 RTAZ system (a total of 1,099 zones in the San Francisco Bay Area).
The calculations of job accessibility use data on the numbers of jobs and workers,
the rates of auto ownership, and OD commuting times by car and public transit. Data on
the numbers of jobs and workers and the rates of auto ownership are extracted from the
Urban Elements of 1990 and 2000 Census Transportation Planning Packages (CTPPs).
CTPPs for San Francisco do not contain data summarized for RTAZs, but they have datasummarized for census traffic analysis zones (CTAZs) which are more disaggregated than
RTAZs. We created an area-weighted factor table using GIS and used it to aggregate the
CTAZ-level data to RTAZ-level data. Data on 1990 and 1998 OD average commuting
times by car and public transit are provided by MTC.
Data on the average commuting times for car and public transit at the spatial levelof RTAZ are not available, so we created these commuting time data using CTPPs, whichcontain data on the number of workers by travel mode and average travel time by travel10

mode at the spatial level of CTAZ, as follows. First, for each commuting mode, multiply
the number of workers by average travel time at the CTAZ level and obtain aggregatedtravel time at the CTAZ level. Second, for each commuting mode, aggregate the travel
time aggregated at the CTAZ level to the RTAZ level and get travel time aggregated at
the RTAZ level. Third, for each commuting mode, aggregate the number of workers at
the CTAZ level to the RTAZ level and obtain the number of workers at the RTAZ level.
And finally, for each commuting mode, divide the travel time aggregated at the RTAZ
level by the number of workers aggregated at the RTAZ level and obtain averagecommuting time at the RTAZ level.
Data for the neighborhood socioeconomic variables in the regression models are
obtained from 1990 and 2000 CTPPs, 1990 Census Summary Tape File 3A (STF3A), and2000 Census Summary File 3 (SF3). The CTAZ-level data for CTPPs, census tract-level
data for STF3A, and census block group-level data for SF3 are converted to RTAZ-level
data. For the income variable, we use mean household income (not median householdincome) since for the RTAZ level, mean household income can be calculated but medianhousehold income cannot be computed with available data.

5

Empirical Results

We present results for visualization in the maps (visualized results) and then results forregression analysis (regression results).

5.1

Visualized Results

Figures 1 to 4 show a series of maps that visualize job accessibility and averagecommuting times which are calculated at the spatial level of RTAZ for car and public
transit in 2000 and that visualize their temporal changes from 1990 to 2000.1 It isapparent that there is considerable inequality in job accessibility and commuting time
between car and public transit and among locations within the San Francisco Bay Area. It
is also apparent that there are substantial spatial variations in the temporal changes in job
accessibility and average commuting time for car as well as for public transit within themetropolitan area.

1

As reference, we present maps that visualize job accessibility and average commuting time for all travelmodes in 2000 and that visualize their temporal changes from 1990 to 2000 in Figures a1 and a2 in Appendix.
General job accessibility in resident zone i (AiG) is calculated as follows:(7)
AiG = α i Aiauto + (1 − α i ) Aitran .

11

Sonoma

Napa
Solano

Santa
Rosa
Marin
OaklandContra Costa

San
Francisco

San
Francisco
Alameda0.00 – 0.50
0.51 – 1.00
1.01 – 1.50
1.51 – 2.00
> 2.00Major highway (2000)
CountySan
Mateo

San Jose

Santa
Clara

N

0

20

40 km

(a) Car(b) Public transit

Figure 1. Job accessibility in the San Francisco Bay Area in 2000

Santa
Rosa
OaklandSan
Francisco< -0.29-0.29 – -0.15-0.14 – 0.00
0.01 – 0.15
0.16 – 0.30
> 0.30Major highway (2000)
CountySan Jose

N

0

20

40 km

(a) Car(b) Public transit

Figure 2. Changes in job accessibility in the San Francisco Bay Area from 1990 to 2000

12

OaklandSanta Rosa

San
Francisco

No data
0.0 – 15.0 (min.)15.1 – 30.0
30.1 – 45.0
45.1 – 60.0
> 60.0Major highway (2000)
CountySan Jose

N

0

20

40 km

(a) Drive alone(b) Public transit

Figure 3. Average travel time to work in the San Francisco Bay Area in 2000

OaklandSanta Rosa

No data< -9.9 (min.)-9.9 – -5.0
-4.9 – 0.0
0.1 – 5.0
5.1 – 10.0
> 10.0Major highway (2000)
CountySan
Francisco

San Jose

N

0

20

40 km

(a) Drive alone(b) Public transit

Figure 4. Changes in average travel time to work in the San Francisco Bay Area from1990 to 2000

13

Figure 1 indicates that job accessibility for public transit users is considerably
lower than that for car users, and that job accessibility varies among locations within themetropolitan area. Since the weighted average of job accessibility is 0.97 (which
represents the ratio of the total number of jobs to the total number of potential workers
looking for jobs in the whole metropolitan area), a job accessibility value above one(which approximates the overall weighted average) can be considered relatively high job
accessibility, and a value below one can be considered relatively low job accessibility.
For people who commute by public transit, job accessibility is very low (a value below0.5) in most zones, whereas it is relatively high in only a limited number of zones aroundthe northeastern area of San Francisco City and the southeastern area of Solano County.2
For people who commute by car, on the other hand, job accessibility is relatively high inmany zones, especially locations surrounding the San Francisco Bay.
Figure 2 illustrates temporal changes in job accessibility from 1990 to 2000
substantially differing among locations within the metropolitan area for car as well as for
public transit. For car users, job accessibility increased markedly in some zones, while itdecreased noticeably in other zones. For public transit users, on the other hand, job
accessibility increased in most metropolitan zones. This latter result suggests improved
public transit systems for the San Francisco Bay Area, which may be related to the factthat transit usage did increase during the 1990s for the first time over the past four
decades as noted earlier.
Figure 3 shows that commuting time by public transit is considerably longer thancommuting time by driving alone and that commuting times for both travel modes differ
considerably by location within the metropolitan area. Commuting times tend to be
shorter in zones around the San Francisco Bay than in zones around the outer suburbs.
Commuting times by public transit in the outer suburbs are especially long, often
exceeding 60 minutes. When the results in Figures 1 and 3 are compared to each other,
the zones showing higher job accessibility appear to overlap with the zones showing
shorter commuting times for car as well as for public transit.
Figure 4 illustrates that there are substantial spatial variations in temporalchanges in average commuting times for both driving alone and public transit during theperiod between 1990 and 2000. The principal finding is that average commuting time by
driving alone increased in most zones in the metropolitan area, while average commuting
time by public transit decreased in a relatively large number of zones, especially locations
around the San Francisco Bay.

2

The relatively high job accessibility zone in the eastern Solano County appears aberrant. With a 30-minutethreshold, the zone has an accessibility value of 1.01, which is the same as the ratio of jobs to workers in thatzone (Figure 1). In other words, with a 30-minute threshold, this zone is beyond the reach of other zones.
When the threshold time lengthens, however, the accessibility value for this zone becomes smaller (forexample, 0.03 for the 45-minute threshold) because jobs in this zone are now accessible for workers residing
in some of the neighboring zones.

14

5.2

Regression Results

Table 2 shows variables and their descriptive statistics, and Tables 3 and 4 present
regression results estimated from the models of average commuting times by driving
alone and public transit, respectively.3 Likewise, the results estimated from the threemodels (OLS, spatial lag, and spatial error models) are presented so that the estimate
variables can be compared to each other. In addition to R2 and adjusted R2 values for OLS,
the tables report the Log-Likelihood, Akaike information criterion (AIC), and Schwarz
criterion (SC) values which can be used to compare goodness-of-fit across the threemodels mentioned above. A higher log-likelihood value indicates a better fit, and a lower
AIC or SC value, which takes into account the number of parameters, suggests a better fit.
Table 2. Variable descriptions and descriptive statistics
1990
Mean

Variable
Dependent variables
Average travel time by driving alone
Average travel time by public transportation
Average travel time by all modes*
Explanatory variables
Accessibility for car users (30 min.)Accessibility for public transit users (30 min.)General accessibility (30 minutes)*% Public transit commuters (excluding taxicab)
Employment density (jobs per km2)
Population density (persons per km2)
% Female civilian labor force% Female-headed households
Mean household income ($)% Persons without high school diplomas% Foreign-born% Non-Hispanic white**% Non-Hispanic black% Non-Hispanic Asian% Hispanic% Other races% Management, business, and financial operations
occupations% Professional and related occupations% Service occupations% Sales and office occupations% Farming, fishing, and forestry occupations% Construction, extraction, and maintenance occupations% Production, transportation, and material moving
occupations**

Std. dev.

2000
Mean

Std. dev.

23.4
47.6
25.4

4.2
13.2
4.7

27.2
53.0
29.1

4.8
16.3
5.2

1.03
0.09
0.91
9.6
2,166
3,040
45.0
16.2
52,722
17.7
19.3
62.3
8.8
13.9
14.4
0.7
16.2

0.43
0.16
0.37
11.4
8,909
3,963
6.6
9.7
23,732
12.8
11.9
23.7
15.4
12.6
13.3
0.6
8.1

1.08
0.14
0.96
10.1
2,243
3,316
45.6
16.4
84,582
16.0
26.1
52.0
7.4
17.7
18.0
4.8
18.1

0.45
0.20
0.41
10.4
9,840
4,207
5.6
9.2
38,739
13.0
14.2
24.5
12.2
15.9
15.9
2.8
8.5

21.2
12.0
28.5
1.7
10.1
10.3

10.0
7.5
6.2
3.3
5.7
6.4

25.6
12.8
25.4
0.5
7.4
10.1

11.3
6.9
5.7
2.7
4.7
7.3

Note: Means are unweighted zonal averages and hence are not consistent with those in Table 1. Observations with no data
are excluded. Public transportation includes bus or trolley bus, streetcar or trolley car, subway or elevated, railroad, andexcludes ferryboat, taxicab, and motorcycle. *Variables used in the models for all travel modes presented in Appendix.
**Variables not included in the models.

3

As reference, regression results estimated from the models of average commuting time by all travel modes
are presented in Table a1 in Appendix.

15

Table 3. Estimation results for average commuting time by driving alone
OLS

Lag-ML

Err-ML

Variable

1990

2000

1990

Constant

17.273(5.26)-5.290(-14.96)0.00001(0.44)
0.00013(4.09)-0.157(-7.01)0.032(1.71)
0.00002(2.09)-0.041(-1.96)0.017(0.58)
0.045(3.98)
0.094(4.14)
0.009(0.48)-0.440(-2.31)
0.142(3.59)
0.099(2.99)
0.107(2.61)
0.248(6.67)
0.226(3.70)
0.261(4.95)

42.141(13.91)-6.135(-17.59)0.00001(0.97)
0.00037(9.73)-0.197(-7.02)-0.041(-1.91)-0.00001(-2.65)-0.058(-2.30)-0.046(-1.76)0.094(6.65)
0.084(4.22)
0.006(0.31)
0.044(0.92)
0.181(4.92)-0.103(-3.30)0.009(0.24)-0.008(-0.21)-0.143(-2.81)0.040(0.81)

2.115
24.917(0.76)(8.79)-2.417-3.228(-7.82)(-9.44)-0.00002 -0.00002(-1.21)(-1.43)0.00005 0.00020(1.86)(5.98)-0.146-0.179(-8.05)(-7.39)0.044-0.025(2.91)(-1.39)0.00001 -0.00002(1.10)(-3.64)-0.031-0.042(-1.84)(-1.95)0.040
0.006(1.74)(0.25)
0.028
0.064(3.04)(5.28)
0.042
0.031(2.31)(1.84)-0.010-0.016(-0.68)(-0.99)-0.3620.019(-2.36)(0.47)
0.101
0.145(3.14)(4.58)
0.119-0.080(4.44)(-3.01)0.086-0.013(2.60)(-0.39)0.206-0.024(6.82)(-0.77)
0.236-0.111(4.76)(-2.54)0.236
0.019(5.54)(0.46)
0.626
0.546(21.87)(17.50)

Job accessibility for car users(30 min)
Employment density(jobs per km2)
Population density(persons per km2)
% Female civilian labor force% Female-headed households
Mean household income ($)% Persons without highschool diplomas% Foreign-born% Non-Hispanic black% Non-Hispanic Asian% Hispanic% Other races% Management, business, andfinancial operations occupations% Professional and related
occupations% Service occupations% Sales and office occupations% Farming, fishing, andforestry occupations% Construction, extraction, andmaintenance occupations
ρ
λ

N
R2
Adjusted R2
Log likelihood
AICSC
LM (lag)
P-value
Robust LM (lag)P-value
LM (error)
P-value
Robust LM (error)
P-value

1,068
0.33
0.32
-2798.85635.75730.1442.63
0.00
40.61
0.00
407.48
0.00
5.46
0.02

1,075
0.38
0.37
-2942.75923.56018.1
326.11
0.00
17.23
0.00
331.92
0.00
23.05
0.00

2000

1990

2000

22.035(8.20)-4.061(-7.74)-0.00006(-3.57)-0.00004(-1.09)-0.151(-7.38)0.047(3.03)
0.00000(-0.09)-0.044(-2.45)0.047(1.78)
0.027(2.37)
0.053(2.46)-0.014(-0.75)-0.373(-2.49)0.068(2.13)
0.093(3.42)
0.044(1.33)
0.176(5.94)
0.179(3.35)
0.218(5.14)

45.112(16.47)-5.021(-9.63)-0.00002(-0.90)0.00019(4.13)-0.225(-8.79)-0.020(-1.07)-0.00002(-4.32)-0.051(-2.26)0.020(0.82)
0.067(4.14)
0.023(1.12)-0.035(-1.80)-0.015(-0.35)0.174(5.27)-0.114(-4.04)-0.037(-1.06)-0.019(-0.60)-0.149(-3.43)0.013(0.31)

0.694(25.28)0.624(19.91)

1,068

1,075

1,068

1,075

-2628.05295.95395.4-2817.1
5674.25773.8-2619.47
5276.95371.4-2809.5
5657.05751.7

Note: t statistics (OLS) and z statistics (Lag-ML and Err-ML) are in parentheses. Bold indicates significant at p<0.05, anditalic denotes significant at p<0.10.

16

Table 4. Estimation results for average commuting time by public transit

Constant
Job accessibility for public
transit users (30 min)
Employment density(jobs per km2)
Population density(persons per km2)
% Female civilian labor force% Female-headed households
Mean household income ($)% Persons without highschool diplomas% Foreign-born% Non-Hispanic black% Non-Hispanic Asian% Hispanic% Other races% Management, business, andfinancial operations occupations% Professional and related
occupations% Service occupations% Sales and office occupations% Farming, fishing, andforestry occupations% Construction, extraction, andmaintenance occupations
ρ

OLS
1990
Coef.
77.358(5.63)-20.677(-5.91)-0.00004(-0.87)-0.00036(-3.04)-0.256(-2.99)-0.178(-2.57)0.00004(1.52)-0.203(-2.47)-0.140(-1.44)0.006(0.16)
0.076(0.98)-0.108(-1.69)-1.010(-1.58)-0.172(-1.09)-0.279(-2.02)0.050(0.31)-0.010(-0.06)0.771(3.35)
0.044(0.22)

2000
Coef.
64.999(4.76)-15.106(-4.86)-0.00008(-1.39)-0.00050(-3.13)-0.190(-1.57)-0.238(-2.77)0.00000(-0.10)-0.159(-1.51)-0.229(-2.21)0.096(1.76)
0.141(1.73)-0.069(-0.94)-0.108(-0.56)0.022(0.14)-0.059(-0.43)0.171(1.08)
0.124(0.75)
0.740(1.77)
1.000(4.71)λN
R2Adjusted R2
Log likelihood
AICSC
LM (lag)
P-value
Robust LM (lag)P-value
LM (error)
P-value
Robust LM (error)
P-value

1,033
0.31
0.30
-3940.97919.88013.7110.37
0.00
29.99
0.00
84.21
0.00
3.83
0.05

1,044
0.26
0.24
-4216.1
8470.18564.2100.38
0.00
26.46
0.00
74.87
0.00
0.95
0.33

Lag-ML1990
Coef.
51.098(3.91)-11.332(-3.32)-0.00004(-0.78)-0.00030(-2.73)-0.213(-2.65)-0.122(-1.88)0.00004(1.53)-0.139(-1.80)-0.092(-1.00)0.010(0.26)
0.069(0.95)-0.072(-1.20)-0.974(-1.63)-0.168(-1.14)-0.178(-1.38)-0.004(-0.03)0.021(0.14)
0.612(2.83)-0.018(-0.10)0.384(9.75)

2000
Coef.
34.812(2.66)-9.107(-3.06)-0.00006(-1.08)-0.00028(-1.85)-0.142(-1.24)-0.178(-2.21)0.00000(-0.01)-0.131(-1.32)-0.175(-1.79)0.096(1.86)
0.131(1.72)-0.022(-0.31)-0.014(-0.08)0.119(0.83)
0.016(0.12)
0.185(1.25)
0.126(0.81)
0.672(1.71)
0.817(4.08)
0.369(9.53)

Err-ML1990
Coef.
69.819(5.25)-20.493(-4.91)-0.00005(-0.85)-0.00040(-3.24)-0.227(-2.61)-0.120(-1.77)0.00005(1.81)-0.113(-1.39)-0.108(-1.06)-0.025(-0.58)0.057(0.71)-0.093(-1.35)-0.669(-1.10)-0.172(-1.13)-0.169(-1.25)-0.007(-0.04)0.059(0.39)
0.633(2.74)-0.012(-0.06)2000
Coef.
55.743(4.03)-12.637(-3.46)-0.00011(-1.65)-0.00045(-2.52)-0.127(-1.03)-0.163(-1.96)-0.00001(-0.23)-0.161(-1.53)-0.181(-1.67)0.096(1.58)
0.134(1.54)-0.038(-0.47)0.016(0.08)
0.144(0.92)-0.021(-0.15)0.172(1.08)
0.129(0.79)
0.792(1.80)
0.893(4.29)

0.382(9.21)

0.378(9.10)

1,033

1,044

1,033

1,044

-3896.30-4173.7
8387.38486.3-3904.8-4182.3
8402.58496.6

Note: t statistics (OLS) and z statistics (Lag-ML and Err-ML) are in parentheses. Bold indicates significant at p<0.05, anditalic denotes significant at p<0.10.

17In the models estimated for both driving alone and public transit, the spatial lag
and spatial error models generate better fits than do the OLS models (as suggested by the
Log-likelihood, AIC, and SC values), and the spatial autoregressive coefficients arehighly significant. In the models estimated for driving alone, LM test statistics suggest
the spatial lag model as the preferable one for 1990 but the spatial error model as thepreferable one for 2000. For both 1990 and 2000, the spatial error models generate thebest fit among the three models, but the results for spatial lag and spatial error models arelargely consistent with one another. In the models estimated for public transit, in both1990 and 2000 LM test statistics suggest the spatial lag model as the preferable one, andthe spatial lag model indeed offers the best fit. Below, we discuss the results for 2000 first
and temporal changes that occurred from 1990 to 2000 subsequently.
First, we discuss the results for 2000. The estimate variables indicate that job
accessibility, the variable of particular interest, is inversely and significantly associatedwith commuting time for driving alone as well as for public transit, after controlling forthe other variables related to urban spatial structure. The degree (represented bycoefficients) and significance (represented by t statistics) of the job accessibility variables
are smaller for the spatial regression models (with consideration of spatial
autocorrelation) than for the OLS models (without consideration of spatial
autocorrelation). This result suggests that an OLS model that does not take into accountspatial autocorrelation is likely to inflate the association between job accessibility andcommuting time. Still, job accessibility estimated from the spatial regression models
exhibit strong associations with commuting time. The degree of the inverse association
between job accessibility and commuting time is considerably greater for public transit
than for driving alone. For example, the estimated spatial lag models indicate that anincrease of 0.41 in job accessibility (one standard deviation of general job accessibility
which combines job accessibility for car and public transit) is associated with a decreaseof 1.3 minutes for driving alone but is associated with a larger decrease of 3.7 minutes for
public transit.
Some of the other urban spatial structure variables also show significant
associations with commuting time. We discuss notable results from the best-fitting
models; the spatial error model for driving alone and the spatial lag model for public
transit. In the model estimated for driving alone, the association between population
density and commuting time is positive and significant, which suggests that greater
congestion eventually slows travel speed for solo driver commuters. In the model
estimated for public transit, on the other hand, the association between population density
and commuting time is negative and significant. This result is understandable byconsidering that public transportation service is usually rather frequent in high density
locations and that transit commuting time is rather unaffected by congestion.
An increase in the proportion of female workers is significantly associated with adecrease in commuting time by driving alone, as is the association between the proportionof female-headed households and commuting time by public transit. These results are
consistent with observations in a number of studies which suggest that women often worknear home because they tend to shoulder domestic burdens and must balance home andwork responsibilities (e.g., Blumenberg, 2004; Hanson and Pratt, 1995; Rosenbloom,

18

1994; Schwanen et al., 2004; Shen, 2000). Mean household income is inversely andsignificantly associated with commuting time by driving alone, which may suggest that
more working families with modest incomes endure long commutes in order to afford
housing. The proportion of residents without a high school diploma is inversely andsignificantly associated with commuting time by driving alone, which suggests that lesseducated people tend to work in a more localized labor market.
An increase in the proportion of black residents is significantly associated with anincrease in commuting time by driving alone as well as by public transit, as is theassociation between the proportion of Asian residents and commuting time by public
transit. These results may result from racial segregation and/or racial minorities residing
in relatively congested areas. An increase in the proportion of Hispanic residents, on theother hand, is significantly associated with a decrease in commuting time by driving
alone, which may suggest that the areas sought by Hispanic workers looking for work in
San Francisco are relatively localized.
Some neighborhood occupation compositions show significant associations with
commuting time, when we use the percentage of residents who work in production,transportation, and material moving occupations as the base case. In the model estimatedfor driving alone, the percentage of residents who work in management, business, andfinancial operations occupations is significantly and positively associated with
commuting time, and the percentages of residents who work in professional and related
occupations and farming, fishing, and forestry occupations are significantly and inversely
associated with commuting time. The shorter commuting time associated with workers infarming, fishing, and forestry occupations is reasonable since these workers tend to live
where they work and hence to have short commutes. The other associations are difficult
to explain without further investigation. In the model estimated for public transit, thepercentage of residents who work in farming, fishing, and forestry occupations and thepercentage of residents who work in construction, extraction, and maintenanceoccupations are positively and significantly associated with commuting time. The
positive association for the percentage of workers in farming, fishing, and forestry is
unexpected, and additional research is required before a satisfactory explanation can begiven. The positive association for the percentage of workers in construction, extraction,and maintenance occupations is understandable, considering that these workers do notoften have fixed workplaces and therefore cannot readily optimize their commutes.
Next, we discuss temporal changes that occurred between 1990 and 2000, based
mainly on the best-fitting models: the spatial error models for driving alone and thespatial lag models for public transit.
Between 1990 and 2000, the degree and significance of the inverse association
between job accessibility and commuting time increased for driving alone but decreased
for public transit. Several factors may account for these changes. During this ten year
interval, commuting time increased noticeably, whereas commuting time had been almost
unchanged or increased at a modest rate until 1990. Indeed, between 1990 and 2000 thenoticeable increases in commuting time occurred for car as well as for public transit as
noted earlier. The increase in commuting time for car may be largely due to exacerbatedcongestion, which presumably makes job accessibility to be a rather important factor

19

associated with commuting time. The increase in commuting time for public transit, on
the other hand, may be substantially influenced by changes in socioeconomic structure
and people’s lifestyles in downtown and its surrounding areas. In fact, the period between
1990 and 2000 experienced increases in the proportion of workers who obtained higher
educational degrees and also the proportion of workers who chose to use public transit
despite their car ownership.4 It can be supposed that some of these people prefer andafford to live in the suburbs while taking public transit to work, which might confoundthe inverse association between job accessibility and commuting time. The in-depth
examination of factors behind these changes may be a topic for future research.
Some of the other urban spatial structure variables show noticeable changes. In
the models estimated for driving alone, the inverse association between employment
density and commuting time is not significant in 2000 but is significant in 1990 (though
the association is insignificant in the OLS and spatial lag models). The degree andsignificance of the association between population density and commuting time are
greater in 2000 than in 1990 (not only in the spatial error model but also in the OLS andspatial lag models), which suggests that population density plays an increasing role in the
explanation of variation in commuting time by driving alone. It should be noted that inboth 1990 and 2000, the association between job accessibility and commuting time are
more significant than the association between employment/population density andcommuting time by driving alone as well as by public transit. This result suggests that in
the explanation of variation in commuting time by travel mode, job accessibility (whichtakes into account the spatial distributions of jobs and workers in addition to travel mode)
is a more important factor than employment and population densities.
The association between the proportion of female-headed households andcommuting time by driving alone shows an inverse and insignificant trend in 2000,
whereas it shows a positive and significant trend in 1990. This result is unexpected andwarrants further investigation to explain why this temporal change took place. By
contrast, the association between mean household income and commuting time by driving
alone shows an inverse and significant trend in 2000, whereas it shows a positive but
insignificant trend in 1990. This temporal change may suggest that during the 1990s, thehigh price of housing in San Francisco forced many working families to live in outskirtsof the city.
While the estimate variables for racial composition are almost consistent in 1990
and 2000 (a few variables are inconsistent for the OLS and spatial lag models though),the significance of some racial estimate variables exhibit notable changes. In the models
estimated for driving alone, the associations between the proportions of Asian residents
and commuting time and between other races and commuting time are insignificant in2000 but are significant in 1990. In the models estimated for public transit, theassociations between the proportions of black residents and commuting time and between
Asian residents and commuting time are significant in 2000 but are insignificant in 1990.
4
For example, the authors’ calculations from the 5-Percent Public-Use Microdata Samples (PUMS) of 1990
and 2000 for the San Francisco Bay Area indicate that between those years, the proportion of public transit
commuters with bachelor’s degrees or higher increased from 42% to 50%, and the proportion of public transit
commuters living in households with two or more cars increased from 44% to 46%. Meanwhile, theproportion of drive alone commuters in multiple-car owning households decreased from 80% to 77%.

20

These temporal changes may have taken place due to the changes in the racialcomposition between 1990 and 2000 (see Table 2).
Similarly, the estimate variables for occupation composition changed during the
1990s, which may be related to rather complicated temporal changes in the associations
between occupation composition variables and commuting time. For example, in themodels constructed for driving alone, the association between the proportion of residents
who work in professional and related occupations and commuting time shows an inverseand significant trend in 2000, whereas it shows a positive and significant trend in 1990.
Also, the association between the proportion of residents who work in farming, fishing,
and forestry occupations and commuting time shows an inverse and significant trend in
2000, whereas it shows a positive and significant trend in 1990. In the models estimatedfor public transit, the association between the proportion of residents who work inconstruction, extraction, and maintenance occupations and commuting time shows apositive and significant trend in 2000, whereas it shows an inverse and insignificant trend
in 1990.
As shown thus far, signs and significance for some variables changed among thethree models and between 1990 and 2000. The results for job accessibility, however, arerobust in the following two points: (1) job accessibility is inversely and significantly
associated with commuting time by driving alone as well as by public transit, and (2) the
degree of this inverse association is greater for public transit than for driving alone. Using
the OLS models, we investigated sensitivity to alternative specifications of job
accessibility at the thresholds of 15, 45, 60, 75, and 90 minutes. We found that the two
above-noted points held true except for a few cases: neither job accessibility at thethreshold of 90 minutes for driving alone nor job accessibility at the threshold of 15
minutes for public transit showed significant association with commuting time in both1990 and 2000. However, these exceptions are unlikely to occur because the assumed
range in the above cases is quite rare, that is, the threshold of 90 minutes is over threetimes of the average commuting time for driving alone, and the threshold of 15 minutes isabout one-third the average commuting time for public transit (see Table 1).

6Conclusions

Using 1990 and 2000 spatial and temporal data at the spatial level of RTAZ for the San
Francisco Bay Area, this empirical study has shown considerable inequality incommuting between car and public transit.
Our results visualized in the maps showed that there was considerable inequality
in job accessibility and commuting time between car and public transit and amonglocations within the metropolitan area. The visualized results also showed that there were
substantial spatial variations in the temporal changes in job accessibility and commuting
time for car as well as for public transit. Here, it must be noted that job accessibility for
car users increased in some zones and decreased in other zones, whereas job accessibility
for public transit users increased in most zones. This result may be related to the slight21

increase in the proportion of public transit commuters that occurred during the 1990s, thefirst such increase in the San Francisco Bay Area since 1960. Also, noteworthy is the factthat commuting time by driving alone increased in most zones, whereas commuting timeby public transit decreased in a relatively large number of zones. These two notablechanges may be related to improvements in public transit systems. Still, our visualized
results as a whole clearly indicate that job accessibility for public transit is considerably
lower than that for car, and commuting time by public transit is markedly longer than thatby car.
Subsequently, we estimated the OLS, spatial lag, and spatial error regression
models that examine the associations between urban spatial structure and commuting
time in general and between job accessibility and commuting time in particular, andcompared the results for driving alone with the results for public transit. The main
findings are summarized in the following three points. First, in both 1990 and 2000 job
accessibility was inversely and significantly associated with commuting time for driving
alone as well as for public transit, after adjusting for the other variables related to urban
spatial structure. The spatial regression models with consideration of spatial
autocorrelation turned out to reduce the degree and significance of the association
between job accessibility and commuting time that had been estimated from the OLS
models without consideration of spatial autocorrelation. Still, job accessibility estimatedfrom the spatial regression models exhibited strong associations with commuting time.
Second, in both 1990 and 2000, the degree of this inverse association was considerably
greater for public transit than for driving alone. And third, when the results werecompared between 1990 and 2000, the inverse association between job accessibility andcommuting time strengthened for driving alone but weakened for public transit. Possible
factors behind these temporal changes were discussed.
With the visualization and regression analyses taken together, the empiricalresults suggest that workers who use public transit are considerably disadvantaged inaccessing jobs as compared to those who drive to work, and also suggest that alreadybuilt environments and residential locations are more important in commuting to work for
public transit users than for car users. The empirical results further suggest that an
improvement in job accessibility can shorten commuting time for car users as well as for
public transit users, but the resultant reduction in commuting time by increasing the samelevel of job accessibility is greater for public transit users than for car users.
The considerable commuting inequality between car and public transit calls formore attention to the equity issue, a principal component of sustainability in urban
development and transportation. Potentially effective approaches to narrowing thecar/public transport disparity include the improvement of public transit mobility andaccessibility and the promotion of transit-friendly urban spatial structure. We point out
that such efforts are helpful not only for people who do not have private vehicles, as isoften the case for disadvantaged people such as low-skilled minority workers and welfare
recipients, but also for people who cannot readily use cars for various reasons or who
prefer using public transit.5 For example, a worker in a household where multiple adults
5

The 5-Percent PUMS of 2000 for the San Francisco Bay Area indicate that 80% of workers who use public
transit to work are in households with one or more cars.

22

share a single car cannot use the car at his/her convenience. As the population ages, there
will be more people who rely on public transit for mobility even though they can afford toown cars. There are also growing movements of reducing automobile dependence andincreasing public transit usage alternatively. Mobility and accessibility enhancements for
public transit are beneficial for all people in a system, not just for those who do not own
cars.
This study was conducted for people who live in the San Francisco Bay Area, ametropolitan area with high public transit usage relative to the US average. The spatial
and temporal dimensions of commuting inequality between car and public transit may besignificantly different in more heavily auto-oriented metropolitan areas, such as Dallas
and Detroit, or in metropolitan areas with much higher public transit usage, such as
London and Tokyo. Systematic comparison of car/public transit commuting inequality
between metropolitan areas with different transport systems and urban spatial structures
is a topic for further examination.

Acknowledgements
This research was supported by research grants from the Asahi Glass Foundation andNihon-Shoken Scholarship Foundation. The authors wish to thank Chuck Purvis of theMetropolitan Transportation Commission for the San Francisco Bay Area for his
assistance in obtaining transportation data used in this research.

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Appendix

Santa
Rosa
OaklandSan
Francisco0.00 – 0.50
0.51 – 1.00
1.01 – 1.50
1.51 – 2.00
> 2.00Major highway (2000)
CountySan Jose< -0.29-0.29 – -0.15-0.14 – 0.00
0.01 – 0.15
0.16 – 0.30
> 0.30Major highway (2000)
County

N

0

20

40 km

(b) Changes in job accessibility
from 1990 to 2000(a) Job accessibility in 2000

Figure a1. General job accessibility in the San Francisco Bay Area

Santa
Rosa
OaklandSan
Francisco
No data
0.0 – 15.0 (min.)15.1 – 30.0
30.1 – 45.0
45.1 – 60.0
> 60.0Major highway (2000)
CountySan Jose

No data< -9.9 (min.)-9.9 – -5.0
-4.9 – 0.0
0.1 – 5.0
5.1 – 10.0
> 10.0Major highway (2000)
County

N

0

20

40 km

(a) Average commuting time in 2000(b) Changes in average commuting timefrom 1990 to 2000

Figure a2. Average travel time to work by all travel modes in the San Francisco Bay
Area

28

Table a1. Estimation results for average commuting time by all travel modes
OLS

Lag-ML

Variable

1990

Constant

21.19427.9685.420
10.37720.96727.847(7.03)(9.63)(2.09)(4.00)(8.42)(10.69)-7.718-7.123-4.162-3.728-4.970-5.196(-21.22) (-20.34) (-12.08)(-10.82)(-9.15)(-9.61)
0.255
0.205
0.142
0.097
0.200
0.106
(16.76) (12.93)(10.27)(6.91)(10.16)(5.23)-0.00008 -0.00011 -0.00004 -0.00007 -0.00007 -0.00008(-6.22)(-8.48)(-3.83)(-6.69)(-4.22)(-4.99)-0.00031 -0.00019 -0.00023 -0.00009 -0.00028 -0.00007(-8.81)(-4.47)(-7.75)(-2.52)(-8.16)(-1.44)-0.088-0.048-0.090-0.065-0.113-0.106(-4.19)(-1.82)(-5.24)(-2.88)(-6.13)(-4.39)-0.007-0.0600.032-0.0400.035-0.029(-0.37)(-2.94)(2.06)(-2.35)(2.22)(-1.65)0.000027 0.000002 0.000015 -0.000006 0.000003 -0.000009(3.91)(0.38)(2.60)(-1.33)(0.42)(-1.88)-0.106-0.100-0.070-0.064-0.085-0.075(-5.25)(-4.18)(-4.17)(-3.18)(-4.71)(-3.52)-0.060-0.008-0.0060.034-0.0120.055(-2.13)(-0.32)(-0.26)(1.62)(-0.44)(2.38)
0.052
0.122
0.025
0.084
0.029
0.103
(4.73)(9.01)(2.74)(7.25)(2.44)(6.53)
0.127
0.091
0.065
0.036
0.077
0.020
(5.86)(4.80)(3.62)(2.26)(3.52)(1.06)
0.112
0.062
0.059
0.031
0.075
0.019
(6.07)(3.43)(3.83)(2.04)(4.03)(1.05)-0.4690.044-0.4340.026-0.3890.007(-2.51)(0.95)(-2.82)(0.67)(-2.56)(0.18)
0.110
0.199
0.084
0.199
0.106
0.263
(3.30)(5.63)(3.05)(6.66)(3.95)(8.45)
0.052-0.037
0.090
0.002
0.108
-0.002(1.71)(-1.23)(3.57)(0.07)(4.21)(-0.08)
0.116
0.121
0.109
0.112
0.110
0.119
(3.38)(3.23)(3.85)(3.55)(3.92)(3.59)
0.192
0.096
0.153
0.081
0.168
0.108
(5.59)(2.78)(5.40)(2.77)(5.98)(3.66)-0.160-0.225-0.081-0.192-0.071-0.217(-3.34)(-4.64)(-2.05)(-4.69)(-1.72)(-5.29)
0.264
0.178
0.215
0.127
0.195
0.130
(5.53)(3.78)(5.45)(3.20)(5.01)(3.28)
0.577
0.552(20.72)(18.81)0.693
0.677(250.17)(23.71)
1,074
1,075
1,074
1,075
1,074
1,075
0.48
0.52
0.47
0.51
-2803.6 -2890.9-2643.1-2749.1-2654.3-2758.05647.35821.95328.35540.25348.75556.05746.85921.55432.95644.85448.35655.5
382.3
346.0
0.00
0.00
74.2
76.1
0.00
0.00
318.1
274.3
0.00
0.00
10.00
4.4
0.00
0.04

General job accessibility(30 min.)% Public transit commuters
Employment density(jobs per km2)
Population density(persons per km2)
% Female civilian labor force% Female-headed households
Mean household income ($)% Persons without highschool diplomas% Foreign-born% Non-Hispanic black% Non-Hispanic Asian% Hispanic% Other races% Management, business, andfinancial operations occupations% Professional and related
occupations% Service occupations% Sales and office occupations% Farming, fishing, andforestry occupations% Construction, extraction, andmaintenance occupations
ρλNR2
Adjusted R2
Log-likelihood
AICSC
LM (lag)
P-value
Robust LM (lag)P-value
LM (error)
P-value
Robust LM (error)
P-value

2000

1990

Err-ML
2000

1990

2000

Note: t statistics (OLS) and z statistics (Lag-ML and Err-ML) are in parentheses. Bold indicates significant at p<0.05, anditalic denotes significant at p<0.10.

29