CSIS Discussion Paper No. 84

Spatio-Temporal Dimensions of Modal Accessibility Disparity:
The Cases of Boston and San Francisco, 1990-2000

March, 2007

Mizuki Kawabata
Center for Spatial Information Science
The University of Tokyo
5-1-5 Kashiwanoha Kashiwa-shi Chiba, 277-8568, Japan
Email: mizuki@csis.u-tokyo.ac.jp

Abstract:
The disparity of accessibility between cars and public transit provides important
information about the degree of auto orientation in urban spatial structure. Using
1990 and 2000 spatial data and a geographical information system (GIS), the present
study examined the degrees and spatial variations of accessibility disparity between
commuting by car and public transit as well as the temporal changes in this disparity
in the metropolitan areas of Boston and San Francisco. In both metropolitan areas
there was a considerable disparity in job accessibility in a comparison between usersof cars and public transit, which turned out to differ substantially by location.
Between 1990 and 2000, regional levels of this accessibility disparity lessened in thetwo metropolitan areas, but the temporal changes in the accessibility disparity varied
considerably among different locations within the metropolitan areas. The
accessibility disparity decreased in the majority of central areas and in a number ofsuburban zones near rail stations, whereas the accessibility disparity increased in a
number of suburban zones near major highways. Improving accessibility for public
transit, relative to that for cars, should be a key strategy for redressing auto-oriented
urban spatial structure, an important objective of sustainable development.

1. Introduction
Redressing auto-oriented urban spatial structure is an important objective ofsustainable development. Low-density, auto-oriented urban development withexcessive automobile dependence has raised a variety of economic, environmental,
and social concerns. It is known that urban areas in the US represent spatial structurewith strong auto orientation. In 2004, the estimated petroleum consumption in the US
was 20.5 million barrels per day, which accounted for 25% of the worlds’ petroleum
consumption; 67% of the US petroleum consumption was expended by thetransportation sector in 2005 (Davis and Diegel, 2006). In 2003, the estimatedcongestion cost for the US was as much as 63 billion dollars, which was derived
from 3.7 billion hours of travel delay and 2.3 billion gallons of wasted fuel (Schrank
and Lomax, 2005). In a sprawling, auto-oriented urban spatial structure, people who
cannot readily use private vehicles are considerably disadvantaged in accessing
opportunities. In recent years, the issue of access equity has received increasing
recognition as an integral component of urban and transportation sustainability1

(Banister, 2002, 2005; Kawabata and Shen, forthcoming; Richardson, 2005).
Location-based accessibility (referred to simply as accessibility in thisstudy) describes an important dimension of urban spatial structure as it denotes theinterrelationship between mobility (performance of transportation systems) and thespatial distribution of opportunities (spatial patterns of land use). When it isdifferentiated by travel mode, accessibility can principally indicate the degree of autoorientation in urban spatial structure. Previous studies that measured accessibility by
car and public transit in US urban areas indicate significantly lower accessibility by
public transit than by car (Blumenberg, 2004; Blumenberg and Hess, 2003;
Kawabata and Shen, 2006, forthcoming; Shen, 1998, 2001). These studies also
indicate considerable spatial variations in accessibility by travel mode; the level ofaccessibility differs by location within a metropolitan area. However, the degree andintra-metropolitan spatial variation of accessibility disparity between cars and public
transit as well as their temporal changes have not yet been sufficiently understood.
This study explores new spatial and temporal dimensions of accessibility
disparity between commuting by car and public transit, which provide valuable datafor developing strategies to redress auto-oriented urban spatial structure. Specifically,
I examine the following two questions. First, to what extent does an accessibility
disparity exist when comparing commuting by car and public transit, and how does itvary among locations within a metropolitan area? Second, how have the degree andspatial variation of the accessibility disparity changed over time?
To answer these questions, I utilize spatial data from 1990 and 2000 and ageographic information system (GIS). The first question is addressed by measuring
and visualizing the cross-sectional disparity of job accessibility by car and public
transit in 1990 and 2000. Differently from most of the related studies, this study
demonstrates explicit measures for the accessibility disparity that can be readily
compared for diverse areas and different times. The second question is answered by
calculating and visualizing temporal changes in the accessibility disparity from 1990
to 2000. In this study, accessibility to jobs (not to hospitals, schools, shops, etc.) is
examined because commuting to work is an important reason for travel in daily life,
and also because the relevant data are available for a comparison of the accessibility
measures. The study areas are the two metropolitan areas of Boston and San

2

Francisco, which are more auto-dependent than typical urban areas in Europe and thedeveloped parts of Asia but less auto-dependent than typical urban areas in the US.
This paper proceeds by first reviewing literature on the accessibility
disparity between cars and public transit in connection with auto-oriented urban
spatial structure (section 2). It next describes methods (section 3) and study areas(section 4), then presents empirical results (section 5), and concludes (section 6).

2. Modal accessibility disparity and auto-oriented urban spatial
structure
Accessibility is generally interpreted as the ease with which activities may bereached from a given location using a particular transportation system (Morris et al,1979). Accessibility thereby represents the interrelationship between the performanceof transportation systems and spatial patterns of land use, which provides important
information about urban spatial structure. Accessibility has been utilized in various
research studies, for example, in accessibility comparisons among different
population groups, such as by education, income, and occupation (Cervero et al,
1999; Wachs and Kumagai, 1973; Wang, 2003); in examinations of spatial mismatch(Blumenberg, 2004; Hess, 2005; Kawabata, 2003), the linkage between urban spatial
structure and travel patterns or behavior (Kockelman, 1997; Levinson, 1998; Shen,
2000), and the sustainability of urban and transportation development (Hemphill et al,
2004; Kwok and Yeh, 2004); as well as in the evaluation of urban and transportation
programs (Geurs et al, 2006; Zhang et al, 1998; Zhu and Liu, 2004). Many scholars
advocate a broader use of accessibility in urban and transportation research andpolicymaking (Cervero et al, 1999; Geurs et al, 2006; Handy, 2002; Handy andNiemeier, 1997; Morris et al, 1979; Wachs and Kumagai, 1973).
Accessibility has been measured with various specifications depending on
the purpose of its use. Accessibility measurements and their applications arereviewed and summarized by Geurs and van Wee (2004), Handy and Niemeier(1997), Harris (2001), and Morris et al (1979), among others. For examining the
disparity of accessibility between cars and public transit in connection with urban
spatial structure, (location-based) accessibility for cars versus public transit is3

particularly useful. Studies that measure accessibility by travel mode for US urban
areas indicate considerable differences in accessibility between cars and public
transit. One body of research calculated the ratio of the cumulative number of jobs
within a 30 minute drive by car to the corresponding number within reach by public
transit. The ratio is found to be 1.3-70.2 for welfare recipients in a total of tenneighborhoods in Alameda and Los Angeles Counties in California (Blumenberg andHess, 2003), 5.2-70.2 for welfare recipients in seven neighborhoods in Los Angeles(Blumenberg, 2004), and 1.7-8.2 for low-income people in a total of 14
neighborhoods in Erie and Niagara Counties in western New York State (Hess 2005).
Another body of research measured job accessibility by travel mode, taking into
account jobs as well as workers competing for these jobs (or spatial competition).
These measures show markedly lower job accessibility by public transit than by car
for low-wage workers in Boston (Shen, 1998), less-educated workers in Boston(Shen, 2001), and general workers in Boston and Los Angeles (Kawabata and Shen,
2006) and in San Francisco (Kawabata and Shen, forthcoming). The studies
measuring accessibility by travel mode also indicate that the levels of accessibility
differ by location within metropolitan areas.
International data suggest that accessibility disparity between cars andpublic transit is prominent in low-density, highly auto-oriented urban spatial structure.
The auto-oriented urban spatial structure is best characterized by US urban areas.
Among major cities in Asia, Australia, Europe, and the US, on average, thepopulation density is lowest in US cities (and Australian cities), and the provision of
public transit services relative to the provision of roads (transit service kilometers perroad kilometer) is also lowest in US cities (Kenworthy et al, 1999). Hong Kong andTokyo are among the cities with the highest densities and most transit service
provisions. For the case of Hong Kong, accessibility is found to be actually muchhigher for public transit than for cars (Kwok and Yeh, 2004). An international
comparison finds that job accessibility by public transit is indeed much lower in
Boston and Los Angeles than in Tokyo (Kawabata and Shen, 2006).
The accessibility disparity between cars and public transit can serve as aprincipal indicator of the degree of auto orientation in urban spatial structure.
However, the extent to which this accessibility disparity exists and varies by location
within a metropolitan area remains relatively unexplored. Furthermore, the extent to4which the degree and spatial variation of the accessibility disparity has changed overtime is not well understood. The spatial variation and temporal changes in the degreeof the accessibility disparity are valuable data for rectifying auto orientation in urban
spatial structure. Such data, for example, help us identify areas with a relatively large
or growing accessibility disparity, and also help us evaluate transportation and land
use plans, such as public transit improvements, transit-oriented development, andland-use mixing. In developing effective plans, a number of other factors should ofcourse be taken into account. Nonetheless, the spatial and temporal dimensions of theaccessibility disparity offer important information that should be worth considering.

3. Methods
The methods used consist of the following three steps: (1) a determination ofcross-sectional measures of the disparity of job accessibility by car and public transit
in 1990 and 2000; (2) a calculation of temporal-change measures of the accessibility
disparity from 1990 to 2000; and (3) an examination of the resulting cross-sectional
and temporal-change measures. The following describes each of these three steps.
The first step is carried out by measuring job accessibility by car and public
transit in 1990 and 2000 and calculating the disparity of job accessibility between
cars and public transit. For this research, the measurement of job accessibility takes
into account the mobility of cars versus public transit as well as the spatial
distributions of jobs (opportunities) and workers (opportunity seekers). The job
accessibility measures are calculated using the following equations, which weredeveloped based on accessibility frameworks proposed by Weibull (1976) and Shen
(1998):Aicar=

j : t ijcar < t 0

Ai

tran

=

Ej

∑

∑

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

j: tijtran < t0kk : t kjtran < t 0

,

(1)

Ej

∑ α W + ∑ (1 − α )Wk : tkjcar < t0

k

k

k

k : tkjtran < t0

5

k

.

(2)

Aicar and Aitran represent the measures for job accessibility in residence zone i for car
commuters and public transit commuters, respectively; tcarij and ttranij are travel times
between zone i and zone j by car and public transit, respectively; t0 indicates a travel
time threshold; Ej is the number of civilian jobs in zone j, and Wk is the number ofcivilian workers (both employed and unemployed) living in zone k; and αk indicatesthe rate of car ownership (the proportion of households with cars) in zone k. The
resultant job accessibility measure for a zone represents the number of jobs withinreach of a given travel time threshold by car (or public transit) for a worker living inthat zone. Note that the population-weighted average of job accessibility, combining
equations (1) and (2) for an entire metropolitan area, becomes the ratio of the total
number of jobs to the total number of workers in the metropolitan area.
The dichotomous approach using the travel time threshold was employedbecause of its interpretability and practicality; the same travel time threshold, or
travel time constraint, generates accessibility measures that are intelligible andcomparable among different modes, locations, and times. In this study, three different
thresholds of 30, 45, and 60 minutes are examined. Thirty minutes is approximately
the average commuting time in 2000 and is therefore considered a representative
threshold.1 In 2000, 45 minutes is about the average commuting time by public
transit,2 and workers with commuting times between 30 and 45 minutes account for
19.1% of commuters in the US nationally (Reschovsky, 2004). Although 60 minutes
is approximately twice as long as the previous average commute, workers with
commuting times between 45 and 60 minutes comprise a measurable amount, 7.4%
in the US nationally (Reschovsky, 2004).
The spatial units of analysis for Boston and San Francisco are the traffic
analysis zone (TAZ) for the 986 zone system (a total of 986 TAZs), and the regional
travel analysis zone (RTAZ) for the 1099 zone system (a total of 1,099 RTAZs),
respectively. These TAZs and RTAZs are the smallest area units for which all thenecessary data were available.
1

The average commuting time in 2000 is 25.5 minutes for the US nationally (Reschovsky,2004), and 28.8 and 29.4 minutes for the Boston Primary Metropolitan Statistical Area
(PMSA) and San Francisco Bay Area, respectively (shown in Table 1).
2
The average commuting time by public transit is 47.7 minutes for the US nationally(AASHTO, 2002), and 44.2 and 46.0 minutes for the Boston PMSA and San Francisco Bay
Area, respectively (shown in Table 1).

6Data on the numbers of workers and jobs and the rates of auto ownershipwere calculated using data from the Urban Elements of the 1990 and 2000 Census
Transportation Planning Packages (CTPPs). The CTPP data for Boston and San
Francisco do not include data summarized for TAZs and RTAZs, respectively, but
contain data summarized for block groups (BGs) and census traffic analysis zones(CTAZs), respectively. Since BGs and CTAZs are in general smaller than TAZs and
RTAZs, the BG-level data were aggregated to determine TAZ-level data for Boston,and the CTAZ-level data were aggregated to determine RTAZ-level data for San
Francisco.
Data on peak-hour origin-to-destination (OD) commuting times by car andpublic transit in 1990 and 2000 (1998 for San Francisco) were provided by theCentral Transportation Planning Staff (CTPS) for Boston and the Metropolitan
Transportation Commission (MTC) for the San Francisco Bay Area. For Boston, the2000 data correspond to the 986 zone system, but the 1990 data correspond to adifferent 790 zone system (an older zone system). Boston’s job accessibility
measures in 1990 were first calculated for the 790 zone system and then converted tomeasures that corresponded to the 986 zone system. The OD commuting time data
for San Francisco from both 1990 and 1998 correspond to the 1099 zone system. The
data from 1998 were the closest to the year 2000 as were available for the 1099 zone
system at the time of the analysis. Job accessibility measures from zones that arecompletely within islands are omitted from the analysis.
The calculated measures for job accessibility by car and public transit arethen used to determine the measures for the disparity of job accessibility between
cars and public transit. The most straightforward approach to measuring thisaccessibility disparity would be the ratio of job accessibility by car to job
accessibility by public transit. In some zones, however, this ratio generated extremely
large and unstable values (over 1,000), which are impractical for comparison. I
therefore took a different approach and measured the disparity of job accessibility
between cars and public transit in zone i (Xi) as follows:

Xi =Aicar − Aitran
Aicar + Aitran .

(3)7

Equation (3) standardizes the difference between job accessibility by car and job
accessibility by public transit to a range from –1 to 1. Figure 1 depicts themeasurement of the accessibility disparity. When accessibility is the same for cars
and public transit, the disparity measure is zero. When accessibility by car is zero,the disparity measure is –1. Conversely, when accessibility by public transit is zero,the disparity measure is 1. As the disparity measure approaches 1, the accessibility
disparity increases.
A tran

A car = A tran

-1

A car < A tran0

A car > A tran

+1

A car

Figure 1. Measurement of accessibility disparity between cars and public transit
The second step is the calculation of temporal changes from 1990 to 2000 in
the disparity of job accessibility between cars and public transit. A temporal change
in the accessibility disparity in zone i (ΔXi) from time t0 to time t1 is measured by

ΔX i = X it1 − X it 0 . A positive value for this temporal-change measure indicates anincrease in the accessibility disparity, whereas a negative value signifies a decrease in
the disparity. Note that a larger absolute value for the negative disparity could
indicate a greater accessibility disparity, but in this study such a case is considered toindicate less accessibility disparity, given that the disparity measures are positive inalmost all zones and also given that greater accessibility by public transit than by car
is the desirable condition to achieve sustainable urban spatial structure.
The third step is an examination of the resulting cross-sectional andtemporal-change measures. First, I examine the overall regional degrees of thedisparity of job accessibility between cars and public transit, along with the temporalchanges in this disparity. For this examination, I calculate population-weighted

8regional averages of the accessibility disparity measures, along with the regional
averages of the measures for job accessibility by car and public transit. The
population-weighted regional averages for job accessibility by car (Acar), job
accessibility by public transit (Atran), and the disparity of job accessibility between
cars and public transit (X) are calculated as: Acar = ∑ (Wi / W ) Aicar ,i =1Atran = ∑ (Wi / W ) Aitran , and X = ( Acar − Atran ) /( Acar + Atran ) , where Wi is the
i =1

number of civilian workers in zone i, and W is the total number of civilian workers in
the whole metropolitan area.
Second, I examine the intra-metropolitan spatial variations and temporalchanges in the degree of the accessibility disparity. This examination uses a GIS toplot the cross-sectional and temporal-change measures for the accessibility disparity.
The plotted measures are shown with major highways as well as with rail lines andstations to examine whether the degree of the accessibility disparity and the temporalchanges in this disparity have any relationship with the locations of thesetransportation systems. Spatial data on the major highways were extracted from the
Census 2000 Topologically Integrated Geographic Encoding and Referencing
system/Line Data. Spatial data on rail lines and stations for Boston and San
Francisco were provided by the CTPS and MTC, respectively.

4. Study Areas
The study areas are the metropolitan areas of Boston and San Francisco. The Boston
metropolitan area used in the study covers all the TAZs in the 986 zone system. The
land area considered in Boston covers 7,300 square kilometers, which is larger than
the 5,200 square kilometers of the Boston Primary Statistical Area (PMSA) andsmaller than the 14,600 square kilometers of the Boston--Worcester—Lawrence
Consolidated Metropolitan Statistical Area (CMSA). The San Francisco Bay Area
includes the nine counties of Alameda, Contra Costa, Marin, Napa, San Francisco,
San Mateo, Santa Clara, Solano, and Sonoma. The land area considered in the San
Francisco study covers 6,900 square kilometers.

9

Table 1. Basic population and transportation characteristics
Boston metropolitan area

2000

18%

6,024

6,784

13%

1,821

12%

3,322

3,535

6%

75.6%

76.3%

0.7

81.2% 80.9%

-0.3

14.2%

13.9%

-0.3

9.5%

9.7%

0.2

24.5

28.8

18%

25.6

29.4

15%

22.9

26.9

17%

23.6

27.3

16%

37.8

44.2

17%

41.2

46.0

12%

2000

2,871

3,398

1,620

% Car a% Public transportation a

Population 16 years and over inlabor force (000) a

Change from

1990

1990
Total population (000) a

Change fromSan Francisco Bay Area

1990 to 2000

1990 to 2000

Means of transportation to work

Mean travel time to work (min.)All modes a
Driving aloneb

Public transportationabb

Source: US Decennial Censuses, The author's calculation using data from 1990 and 2000 CTPPs.
Note: The Boston metropolitan area for source a is the Boston PMSA. Public transportation for themeans of transportation to work (from source a) includes taxicab and ferryboat, but public
transportation for the mean travel time to work (from source b) excludes taxicab and ferryboat (i.e.,
public transportation includes the categories of bus or trolley bus, streetcar or trolley car, subway orelevated train, and railroad).

Table 1 presents the basic population and transportation characteristics ofthe metropolitan areas of Boston and San Francisco. In 2000, Boston and San
Francisco accommodated 3.4 and 6.8 million people, respectively, and 1.8 and 3.5
million workers (persons in the labor force), respectively. Between 1990 and 2000,
the population and labor force increased substantially in both metropolitan areas. In
Boston and San Francisco, the population grew by 18% and 13%, respectively, andthe number of workers increased by 12% and 6 %, respectively.
Both metropolitan areas show considerably high auto dependency. In 2000,
the proportions of car commuters in Boston and San Francisco were 76.3% and80.9%, respectively, while the proportions of public transit commuters were only
13.9% and 9.7%, respectively. Interestingly, between 1990 and 2000 the modal
proportions changed only slightly. In Boston, the car share increased by only 0.7
points (from 75.6% to 76.3%) and the public transit share decreased by 0.3 points(from 14.2% to 13.9%). In San Francisco, the car share, although only slightly,

10

decreased by 0.3 points (from 81.2% to 80.9%) and the public transit share increased
by 0.2 points (from 9.5% to 9.7%). In fact, more detailed categories (not shown in
the table) indicate that in Boston as well as in San Francisco, the share of rails(including streetcar, trolley car, and subway and elevated rails) increased while theshare of buses decreased.3 These trends contrast with relatively large decreases in
the public transit shares observed in both areas during the previous decade. Between
1980 and 1990, the public transit share decreased by 2.3 points (from 12.9% to10.6%) in the Boston CMSA (Rossetti and Eversole, 1993) and also decreased by 1.9
points (from 11.4% to 9.5%) in the San Francisco Bay Area (MTC, 2002).
In 2000, the average commuting times in the metropolitan areas of Boston
and San Francisco were 28.8 and 29.4 minutes, respectively. The data by travel mode,
however, indicate that commuting times differ considerably between cars and public
transit. In Boston and San Francisco, the average commuting times by public transit(44.2 and 46.0 minutes, respectively) were markedly longer than those by driving
alone (26.9 and 27.3 minutes, respectively). During the period from 1990 to 2000,
both metropolitan areas experienced substantial increases in travel time to work. The
average commuting time in Boston and San Francisco lengthened by 18% and 15%,
respectively. The data by travel mode reveal that the average commuting time grewfor both cars and public transit. In Boston and San Francisco, the average commuting
times by driving alone increased by 17% and 16%, respectively, and the averagecommuting times by public transit grew by 17% and 12%, respectively.

5. Results
First, I present results for the overall degree of disparity in job accessibility between
cars and public transit by region, along with the temporal changes in this disparity.
Next, I present results for the intra-metropolitan spatial variations and temporalchanges in the degree of the accessibility disparity.

3

In the Boston PMSA and San Francisco Bay Area, between 1990 and 2000 the proportionsof rail commuters increased from 8.5% to 9.4% and from 3.4% to 4.0%, respectively, while
the proportions of bus commuters decreased from 5.4% to 4.1% and from 5.9% to 5.4%,
respectively.

11

Table 2 presents the population-weighted regional averages for the measures ofthe disparity of job accessibility between cars and public transit. For reference, Table
2 also presents the population-weighted regional averages for the measures of job
accessibility for cars and public transit. As described previously, the standardized
measures of the accessibility disparity range between –1 and 1.
The overall regional measures indicate a considerably large disparity of job
accessibility between cars and public transit. The regional measures also indicate
surprisingly similar results for Boston and San Francisco. In 2000, the regional
measures of the accessibility disparity for the 30-minute threshold were 0.750 and
0.775 in Boston and San Francisco, respectively. The fact that these values are close
to 1 indicates a markedly lower job accessibility by public transit than by car. This
result is in contrast to the case of Hong Kong, for which a similarly standardized
measurement in 1996 was –0.853 (Kwok and Yeh, 2004).4 The fact that this value isclose to –1 denotes a much higher accessibility by public transit than by car. Hong
Kong may be an extreme case with predominant public transit systems, but theconsiderable difference is worth noting.
In both metropolitan areas in this study, the regional measures for the
disparity of job accessibility between cars and public transit diminish as the travel
time threshold increases, since as the threshold lengthens the regional measures forjob accessibility by public transit increase while the regional measures for job
accessibility by car decrease or remain stable. This trend suggests that a longer
threshold time enables a relatively larger number of public transit users, compared to
car users, to access a greater number of jobs. The regional disparity measures for the
60-minute threshold are the smallest among the three threshold times, but all thedisparity measures for this longer threshold are still greater than 0.5, indicating large
accessibility disparity.

4

The original measure in Kwok and Yeh (2004) is 0.853. Since their calculation forstandardization uses (Atran - Acar)/(Acar + Atran) instead of (Acar - Atran)/(Acar + Atran), thisoriginal measure is equivalent to –0.85 in this study.

12

Table 2. Regional averages of the measures for job accessibility disparity between
cars and public transit and job accessibility for cars and public transit
Boston metropolitan area
Change from

1990

2000

Disparity measure

0.826

0.750

Job accessibility for

1.143

1.158

0.109

0.165

Disparity measure

0.765

0.649

Job accessibility for

1.117

1.134

0.149

0.241

Disparity measure

0.712

0.578

Job accessibility for

1.101

1.115

0.185

0.298

San Francisco Bay Area
Change from

1990

2000

0.845

0.775

1.028

1.057

51%

0.086

0.134

56%

-15%

0.743

0.620

-17%

1.011

1.042

62%

0.149

0.244

64%

-19%

0.668

0.534

-20%

1.006

1.035

0.200

0.314

1990 to 2000

1990 to 2000

30 minutes

cars
Job accessibility for
public transit

-9%
1%

-8%
3%

45 minutes

cars
Job accessibility for
public transit

2%

3%

60 minutes

cars
Job accessibility for
public transit

1%
61%

3%
57%

It is interesting to find that between 1990 and 2000, the disparity of job
accessibility between cars and public transit at the regional level lessened for both
Boston and San Francisco. The regional disparity measures for the 30-minutethreshold, for example, decreased by 9% (from 0.826 to 0.750) in Boston and 8%(from 0.845 to 0.775) in San Francisco. These decreases were the result of greater
improvements in the regional measures for job accessibility by public transit
compared to job accessibility by car. The magnitude of the decreases is greater for
longer thresholds. The regional measures of the accessibility disparity with the
60-minute threshold, for instance, declined by 19% (from 0.712 to 0.578) in Boston

13and 20% (from 0.668 to 0.534) in San Francisco.
The cross-sectional and temporal-change measures for the disparity of job
accessibility between cars and public transit can be plotted on maps. Figures 2 and 3
assist in visualizing the measures with the 30-minute threshold for the metropolitan
areas of Boston and San Francisco, respectively. Each figure presents four maps: (a)cross-sectional measures for the disparity of job accessibility between cars and public
transit in 2000 (with major highways); (b) cross-sectional measures for theaccessibility disparity in 2000 (with rail lines and stations); (c) temporal-change
measures for the accessibility disparity from 1990 to 2000 (with major highways);and (d) temporal-change measures for the accessibility disparity from 1990 to 2000
(with rail lines and stations). Maps for the measures with the 45- and 60-minutethresholds are not presented but were examined.
Figures 2a, 2b, 3a, and 3b indicate that the degrees of the disparity of job
accessibility between cars and public transit differ substantially among locations
within the metropolitan areas. In general, the accessibility disparity is relatively small
around urban core areas, but relatively large in suburban areas.5 The majority ofzones in the central business districts in Boston, downtown San Francisco, anddowntown Oakland exhibit disparity measures of 0.5 or less (relatively low
accessibility disparity). This result is reasonable given that these areas are served bycomparatively well-developed public transit systems. The majority of suburban zones,on the other hand, display disparity measures greater than 0.5. Of particular note is
the fact that many suburban zones show disparity measures greater than 0.9(extremely high accessibility disparity). A number of such suburban zones withextremely high accessibility disparity are found near major highways, while anumber of suburban zones with lower accessibility disparity are observed near rail
stations. These trends are consistent when examined with the 45- and 60-minutethresholds.

5

The negative disparity measures in 2000 in the western and eastern zones of the San
Francisco Bay Area are unnatural. This result is due to the zones’ scarce population andrelatively large size which destabilized job accessibility measures. This is a limitation ofusing TAZs or RTAZs. Since these zones are sparsely populated, however, the result does not
significantly distort the population-weighted regional measures.

14Lawrence

Lawrence

LowellLowell

495

Boston95

MarlboroughBostonMarlboroughN

Plymouth

Plymouth

Disparity measure-0.05 – -0.01
0.00 – 0.25
0.26 – 0.50
0.51 – 0.80
0.81 – 0.90
0.91 – 1.00
Major highway

0

20

Railroad
Station (-1990)
New station (1990-2000)

40 km(b) Cross-sectional measures in 2000
(with rail lines and stations)(a) Cross-sectional measures in 2000
(with major highways)

Lawrence
LowellLowellLawrence

495

Marlborough95

BostonPlymouth

PlymouthChange indisparity measure< -0.20-0.20 – -0.11-0.10 – -0.01
0.00
0.01 – 0.10
0.11 – 0.20
> 0.20Major highway
1 Dot = 20 New jobs

Framingham
stationBostonTauntonRailroad
Station (-1990)
New station (1990-2000)(c) Temporal-change measuresfrom 1990 to 2000
(with major highways)(d) Temporal-change measuresfrom 1990 to 2000
(with rail lines and stations)Figure 2. Disparity of job accessibility between cars and public transit in the Boston
metropolitan area (30 minutes)

15

Santa Rosa

Santa Rosa
101

OaklandSan
FranciscoN

OaklandSan
Franciscodisparity measure-0.05 – -0.01
0.00 – 0.25
0.26 – 0.50
0.51 – 0.80
0.81 – 0.90
0.91 – 1.00
Major highway

Silicon Valley

Silicon Valley
101

0

Railroad
Station (-1990)
New station (1990-2000)

40 km(a) Cross-sectional measures in 2000
(with major highways)(b) Cross-sectional measures in 2000
(with rail lines and stations)

Santa Rosa

Santa Rosa

101

Pittsburg/Bay
Point station

Berkeley

12th Street
Oakland City
Center station

OaklandSan
Francisco

San
Francisco

Change indisparity measure< -0.20
Silicon Valley-0.20 – -0.11
101-0.10 – -0.01
0.00
0.01 – 0.10
0.11 – 0.20
> 0.20Major highway
1 Dot = 20 New jobs (c) Temporal-change measuresfrom 1990 to 2000
(with major highways)

Silicon Valley

Railroad
Station (-1990)
New station (1990-2000)(d) Temporal-change measuresfrom 1990 to 2000
(with rail lines and stations)Figure 3. Disparity of job accessibility between cars and public transit in the San
Francisco Bay Area (30 minutes)

16

Figures 2c, 2d, 3c, and 3d illustrate the temporal changes in the disparity of
job accessibility between cars and public transit from 1990 to 2000. A negative value
indicates a decrease in accessibility disparity, and conversely, a positive value
indicates an increase in accessibility disparity. The maps reveal that the temporalchanges in the degree of accessibility disparity vary considerably by location within
the metropolitan areas. It is a favorable finding that a large number of zones
experienced a decrease in accessibility disparity. However, there are also zones thatexperienced an increase in accessibility disparity. Zones with either decreased orincreased accessibility disparity are widely scattered within the metropolitan areas,
but the following three patterns are observed in general.
The first pattern is that the disparity of job accessibility between cars andpublic transit lessened in the majority of central zones. In Boston, the great majorityof zones inside and around Route I-95 show diminished accessibility disparity. In
San Francisco, most zones around downtown San Francisco and downtown Oakland
indicate decreased accessibility disparity. These results suggest that dependence on
public transit increased in areas where dependence on public transit had been known
to be relatively high. Indeed, MTC (2004) reports that there were noticeable
increases in public transit commuting to downtown San Francisco and downtown
Oakland between 1990 and 2000. The public transit shares of commuting to
downtown San Francisco and downtown Oakland increased from 46.6% to 49.0%
and from 20.2% to 24.1%, respectively, and the number of public transit users
commuting to downtown San Francisco and downtown Oakland grew by 18% and45%, respectively.
The second pattern is that the disparity of job accessibility between cars andpublic transit increased in a number of suburban zones near major highways. In
Boston, there was also an increase in a number of suburban zones without commuter
rails nearby. Relatively large increases are found, for example, in areas around RouteI-495 in Boston and in some parts of Silicon Valley and northern areas in San
Francisco. These results suggest that reliance on public transit deteriorated in areas
where reliance on public transit had been known to be relatively low, in contrast to
the trend for the central areas.
The third pattern is that the disparity of job accessibility between cars and17

public transit lessened in many suburban zones near rail stations, whether existing or
newly-developed. In Boston, many of the suburban zones with decreased
accessibility disparity are found around rail stations. In San Francisco, many
suburban zones around rail stations experienced lessened accessibility disparity.
During the 1990s, both metropolitan areas developed a fairly large number of newrail lines and stations. In Boston, the regional commuter rail system opened the
Middleborough/Lakeville Line and Providence/Stoughton Line in the southern
suburbs, along with a number of stations. In San Francisco, the Altamont Commuter
Express and Amtrak Capitol Corridor began operation, and quite a few stations wereadded to the regional public transit systems, including the Bay Area Rapid Transit(BART), Caltrain, and Santa Clara Valley Transportation Authority. Accordingly, theaccessibility disparity diminished in many zones around these newly-opened raillines and stations, suggesting a heightened reliability for suburban public transit.
Moreover, MTC (2004) documents that during the 1990s, San Jose, which developed
new light rail extensions and stations, attracted a substantial number of riders for
public transit. Between 1990 and 2000, the public transit share of commuting to jobs
in downtown San Jose increased from 4.7% to 7.2%, and the number of workers
commuting by public transit to downtown San Jose rose by 60%.
It has to be noted that in some suburban zones near rail stations, thedisparity of job accessibility between cars and public transit increased with the 30
minute threshold, but in many of such zones the accessibility disparity decreased
with the longer thresholds of 45 and 60 minutes. This result may suggest that withlonger travel time thresholds, suburban residents who take public transit to commute
to work became more likely to attain jobs in central areas or suburban employment
centers than those who use cars for commuting.
The fact that the accessibility disparity for suburban residents increased forshorter travel times but decreased for longer travel times may largely have resulted
from an increase in road congestion, improvements in public transit, or both. An
example is the area around Framingham Station (which already existed in 1990) in
the western suburbs of Boston (see Figure 2d). In zones surrounding this station, theaccessibility disparity increased with the 30-minute threshold but decreased with the
45- and 60-minute thresholds. Since one way travel time between Framingham
Station and Back Bay Station/South Station in downtown Boston by commuter rail is

18

approximately 30-50 minutes (MBTA 2007), it is likely that residents in the
Framingham Station area can reach highly-concentrated jobs in downtown Boston
within 45 to 60 minutes by commuter rail. In this case, the decreases in theaccessibility disparity for the longer thresholds might largely reflect an increase incongestion, which impedes the mobility of cars.
Another example is the area around Pittsburg/Bay Point Station (which
opened in 1996) in the eastern suburbs of San Francisco (see Figure 3d). In zones
surrounding this station, the accessibility disparity increased with the 30-minutethreshold but decreased with the 45- and 60-minute thresholds. Since one way travel
time between Pittsburg/Bay Point Station and 12th Street Oakland City Center
Station (downtown Oakland) by BART is approximately 40 minutes (BART 2007), it
is likely that as of the station’s opening in 1996, residents in the Pittsburg/Bay Point
Station area became able to access jobs in downtown Oakland within 45 to 60
minutes using BART. In this case, improvements enhancing the mobility of public
transit might have played a major part in the decreases in the accessibility disparity
with the longer thresholds.

6. Conclusion
This empirical study has examined the spatial and temporal dimensions ofaccessibility disparity between car and public transit commuting in the metropolitan
areas of Boston and San Francisco. The results for the two metropolitan cases were
surprisingly similar.
The empirical measures for the disparity in job accessibility between cars
and public transit indicated considerably lower job accessibility by public transit thanby car. It was a favorable finding, however, that between 1990 and 2000 theaccessibility disparity at the regional level decreased in both metropolitan areas.
Such temporal decreases in the accessibility disparity may be related to the fact thatthe public transit shares of commuters remained largely unchanged between 1990
and 2000 (Table 1), in contrast to the public transit shares themselves that apparently
declined during the previous decade as noted earlier.

19Within the metropolitan areas, there were considerable spatial variations andtemporal changes in the disparity of job accessibility between cars and public transit.
Major findings can be summarized as follows. First, in general the disparity of job
accessibility between cars and public transit was relatively low in urban core areas
but relatively high in suburban areas. In fact, extremely high accessibility disparity(disparity measures greater than 0.9) was found in a large number of suburban zones.
Second, between 1990 and 2000 the accessibility disparity lessened in the majority ofzones in central areas with relatively well-developed public transit systems and also
in many suburban zones near rail stations, whether existing or newly-developed.
Moreover, between 1990 and 2000 the accessibility disparity grew in a number ofsuburban zones near major highways, and in Boston it also grew in a number ofsuburban zones without nearby commuter rails.
Accordingly it is suggested that the provision of public transportation
systems can lessen the disparity of accessibility between cars and public transit not
only in central areas but also in suburban areas. It is also suggested that the
improvement of accessibility for public transit relative to that for cars is important inrectifying auto-oriented urban spatial structure. Although overall regional disparity of
job accessibility between cars and public transit mitigated during the 1990s, theaccessibility disparity is still considerably large. The augmentation of accessibility
for public transit relative to that for cars should be a key strategy for redressing
auto-oriented urban spatial structure, an important objective of sustainable
development.
The empirical measures from this study can be used, for example, to identify
areas with a growing disparity of job accessibility between cars and public transit aswell as increasing job opportunities, which can be considered priority areas for
public transit improvements.6 The empirical measures can also be used to examine
the extent to which public transit improvements and land use changes would
decrease the disparity of job accessibility between cars and public transit.
Comprehensively evaluating conceivable scenarios is beyond the scope of this paper,
but several cases can be demonstrated. Suppose, for instance, public transit systems
6

Such zones are found, for example, in Lowell, Marlborough, Plymouth, and Taunton in the
Boston metropolitan area and in Berkeley, Santa Rosa, and some Silicon Valley cities,
including Mountain View and Sunnyvale, in the San Francisco Bay Area (see Figure A1 in
Appendix).

20

are improved in the city of Lawrence, part of which actually experienced an increasein the disparity of job accessibility between cars and public transit. If the
improvement in public transit systems reduces OD transit commuting time by 10% ineach origin zone in the city of Lawrence, the overall accessibility disparity for thiscity would decrease by 3.7% (from 0.856 in 2000 to 0.824). If, besides this public
transit improvement, the number of jobs in each zone in the city grows by 10%, theoverall accessibility disparity for the city would decline further, by 4.6% (from 0.856
in 2000 to 0.817).
This study examined the cases of Boston and San Francisco, which haverelatively low auto dependency by US standards but relatively high auto dependency
by international standards. Auto orientation in urban spatial structure is dominantly
strong in the US, whereas auto orientation is growing in many other parts of theworld (Giuliano et al, 2004). One direction of future research will involve an
examination of other domestic and international urban areas and a comparison of theresults. Another direction of future research will entail an examination of whether
decreases in accessibility disparity between cars and public transit significantly
augment public transit use. Such future research will provide more comprehensive
data to help develop strategies to redress auto-oriented urban structure, not only foradvanced nations but also for developing countries.

Acknowledgements
This research was supported by research grants from the Japan Society for the
Promotion of Science and the Asahi Glass Foundation. The author thanks Masahiro
Hachimori, Yuzo Maruyama, and Qing Shen for their helpful comments, and Clinton
Bench, Thomas J. Humphrey, Charles Purvis, and Paul Reim for their assistance withobtaining transportation data.

21

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Appendix
Santa Rosa

Lawrence
Lowell101

Berkeley
Marlborough495
95

Oakland

BostonN

SanFrancisco

Change indisparity measure< -0.20-0.20 – -0.11-0.10 – -0.01
0.00
0.01 – 0.10
0.11 – 0.20
> 0.20Major highway
1 Dot = 20 New jobs

Plymouth
Silicon Valley
101

Taunton
0

20

40 km

0

(a) Boston metropolitan area

40 km(b) San Francisco Bay Area

Figure A1. Changes in disparity of job accessibility between cars and public transit
and new jobs from 1990 to 2000 (30 minutes)

26