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Walk Access to BART and Residential

Density

Sherman Lewis and Zoe Roller May 8, 20152787 Hillcrest Ave.Hayward CA 94542510-538-3692sherman@csuhayward.us

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Sherman Lewis and Zoe Roller Walk Access to BART and Residential DensityMay 8, 20152787 Hillcrest AvenueHayward CA 94542510-538-3692sherman@csuhayward.us

Abstract

This paper used new approaches and new Bay Area Rapid Transit District (BART) data to analyze density and walk distance to public transit. We looked at walk access to BART and at density around BART stations with high levels of walk access. BART’s large survey of riders found an average walk from home to station of a little over half a mile. We produced maps showing densities of block groups around high-walk stations. We found no correlation between population densities of block groups close to stations for the 16 highest walk-access stations. When three Central Business District (CBD) stations were removed, however, the correlation improved. When block group census data on means of journey to work by transit, bicycle, and walk was used, the correlation with residential density was very strong. We looked at the potential for walk distances longer than the conventional half mile and found that roughly 16 percent of walk access is 0.89 miles or more. Planners should consider longer walk distances, along with other environmental design and economic incentives to shift travel mode.

Objective

Walking and other non-auto modes are important for health and for reducing dependence on automobiles and lowering carbon emissions. Dense residential neighborhoods support walking and transit over dependence on private vehicles. Many factors influence mode choices, but this paper only looks at a short list: distance walked to transit, residential density around high walk-access stations, non-auto commute modes, and planning guidelines for walk access. This research supports other papers that have found walk distances to transit over half a mile, but not a good correlation with density around stations. Density was, however, highly correlated with non-auto modes in general.

The purpose of the paper is to use the data available from the Bay Area Rapid Transit District (BART) as it relates to density and walk distance. It does not attempt to discuss other variables that also play a role in access to transit, such as availability and cost of parking, design quality of walk paths, and total commute travel time.

Literature review

A small body of literature exists on walk distance to transit; also referred to as catchment areas. Some of this literature includes reports on

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surveys of rapid transit riders about their walk distance from home to rapid transit (home-access walk trips). Other topics within this field of study include walks to all kinds of transit, walks at the destination end, and walking in general. Walk to transit is also referred to as a stage in a multi-modal trip. Methodological issues include the use of a radius to define a buffer around a station when walking paths are not direct, quality of self-reporting by respondents, and the use of wearable GPS reporting by respondents.

Moran’s literature review “Walking the Walk” (2013) looks at a survey of bus riders in Austin, Texas. Moran challenges the rule of thumb that people will not walk more than five minutes to reach public transit. She concludes that walk access is in fact far more varied, and recommends that further research should not rely on the five minute assumption.

In a study of a Toronto suburb, Crowley, Shalaby, and Zarei (2009) look at the relationships among the built environment, mode choices, and distance to public transit. They found that transit ridership could flourish in low-density areas, even though most riders who walked went only 0.2 miles or less to stations. They also found that car ownership increases as residents live further from a station.

Bergman, Gliebe, and Strathman (2011) look at the WES commuter rail in Portland, Oregon, and analyze inter-modal transit choices by distance, trip time, and rider income. They found that median walk distance was 0.54 miles, and over ten minutes. This study mentions that walk access is positively correlated to high densities, and suggests that urban design factors could increase walkability.

Research is usually framed in context of helping planners provide transit service, with survey results as a basis for advice. Usually, many variables in addition to walk distance are considered in order to understand a simple act with complex causes. These variables include individual characteristics (income, education, gender, disability, attitudes, vehicle availability), walk route factors (weather, social quality of route, mixed land uses, barriers along route, walk distance, population density), transit features (station spacing, frequency, speed, transfers, wait time, distance to Central Business District (CBD), parking at station), and destination features (employment density, walking at destination).

Besides helping planners improve transit and walkability, research is often motivated by a desire to reduce auto dependency for environmental reasons, to promote a more sociable urban alternative to suburbia, to improve pedestrian safety, and to increase walking for health.

Since the 1970s, planning standards or guidelines for walking distances, catchment area, or service area to rapid transit stations commonly used a distance of 400 meters (quarter of a mile) or 800 meters (half a mile), which became a conventional wisdom with little empirical basis. More recent discussion are more nuanced, considering not just some average walk distance, but a larger proportion, 75 to 90 percent, of walkers, and measuring the fall off or rate of decay as the distance gets longer.

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Some older surveys reporting short walk distances refer to access to buses, which have a much shorter walk distance than rapid transit. Since the 1990s, surveys of people walking to rapid transit stations have generally revealed longer walk distances, usually over half a mile, especially as a larger share of walkers is taken in.

This summary is largely taken from more detailed discussions by El-Geneidy et al. (2014), Agrawal, Schlossberg, and Irvin (2008) and other articles in the references. In addition, the literature has usually not included how walk access to rapid transit relates to personal travel time budgets, locational decisions, pricing incentives and subsidies in the larger economy, and national household and time use databases.

Survey results from the literature are included below along with those from this report. No studies of home origin walk to rapid transit have ever been replicated and all use different variables and methodologies. There is no database where results are compiled into a consistent frame of reference. Some common approaches and systematic compilation into a database would be helpful.

The literature on individual neighborhoods around transit stops, with land use maps and details, is almost non-existent. Neighborhoods are usually aggregated and discussed generically and statistically. A recent example is report, The WalkUP Wake-Up Call: Boston,1 has a real estate development emphasis. One of the sponsors, LOCUS / Smart Growth America, promotes investment in mixed use redevelopment. The study of Boston named 71 “WalkUPs” or “walkable urbanism” of various kinds and defined their boundaries and areas, which included many land uses besides neighborhoods, but not the number of residents. The report makes references to density in general, but no densities of specific neighborhoods. It recommends a gross density for residential units of over 8 per acre with a quarter mile. Since the WalkUPs have many non-residential land uses, there is no way to tell what the density is. The report says there are about 88,000 people per established WalkUP. When I asked Smart Growth America about what this meant, they informed me that the figure was the metro population divided by the number of WalkUPs, and the average WalkUP had under 10,000 people.2 The WalkUPs average about 28 people per acre, including non-neighborhood uses. The report uses Walk Score, intersection density, and other variables, but does not cover how they relate to walk access to transit.

This paper contributes new information about walk distances in more detail than other reports, including a list of stations, the number of walk access respondents, and the mean, median, and standard deviation walk time from home access, showing the large variation in amount of walk access and walk distance that is often hidden in aggregate data. The study

1 Christopher Leinberger and Patrick Lynch, George Washington University School of Business. 2015, http://www.smartgrowthamerica.org/documents/walkup-wake-up-call-boston.pdf 2 Patrick Lynch, plynch@email.gwu.edu, email, 4/20/2015

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compiles results from previous studies and combines them with new analysis. The paper has maps showing block group densities in the half mile around high-walk-access stations, and compares them to walk access and to non-auto mode share of the journey to work, also not found in the literature. The paper has a discussion of how walk access to transit relates to planning.

Methodology

BART is an urban transit system serving the San Francisco Bay Area. The 2008 BART Station Profile Study (BART, 2008) and BART’s research department provided rider survey data on median, mean, and standard deviations of walk distance from home origins to BART stations (details in Table 3 in the Data Appendix).3

BART conducted interviews at many different hours of the day and in a number of languages. The BART Station Profile survey included 5,974 riders who walked from home. We believe their samples are representative of the areas around the stations.

The paper uses unweighted data from BART and does not go into methodological questions about it. The BART data is probably robust due where it has 100 or more respondents per station, a very large sample for individual stations from any rapid transit system. There are also limits on the validity of accuracy block groups with small populations. The purpose of the paper is not statistical accuracy but to approximate findings for policy purposes.

To look at decay rates or fall off of walk access with distance, we used station data for median times and standard deviations and plotted second order polynomial fit lines, as shown in Figure 1. Standard deviations were not used as statistics, but as useful number for a policy estimate. Strict accuracy about density is not useful because design and pricing factors are also important.

To look at density around stations, we selected the sixteen stations with more than 100 respondents who walked from home to the station. We also used census data on block group population and Google satellite images of land use around BART stations to determine density.

Census tracts were not used because they are often too large to use for walking distances. Cervero et al (1995, p. 41) says “In lower-density areas...census tracts generally increase in size...In some suburban and exurban parts of the Bay Area, for instance, large amounts of open space are within several census tracts, producing large territorial units. Even if one access trip origin is in one of these zones, using our criteria, the zone will be added to the catchment, thus skewing the estimate of land coverage. ...Virtually all census tracts that met the catchment criteria had far more land that was developed than undeveloped. Still, ideally, smaller geographic units, like block groups or even blocks, would be used in defining

3 http://www.bart.gov/sites/default/files/docs/2008StationProfileReport_web.pdf

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catchments. ...1The use of census tracts for defining catchment areas posed problems for studying walk-on trips in suburban areas where census tracts can be large, often with dimensions well beyond the one-quarter- to one-half-mile distance normally considered to be the maximum distance Americans will walk. In more urbanized areas, especially downtown San Francisco, census tracts (sometimes as small as four or five city blocks) are more suitable for studying the catchments for pedestrian access trips.”4

The map on Pleasant Hill from Cervero et al. using tracts illustrates the problem. The shaded area is well over half a mile and even goes beyond the next station.

Another issue is whether to define the area by distance or by some percent of all the walk access trips using the station. The larger the percent, the larger the area around the station and the more likely the density will be lower. Cervero et al. used both census tracts and 90 percent of the walk access, resulting in larger areas with lower densities: “The catchment areas for walk-on trips to BART stations were defined as the census tracts encompassing the origins of 90 percent of all access trips made by foot.”

Block groups also have problems. They are lumpy; they are irregularly shaped and meander inside and outside the half-mile circle. We included data from block groups which are more than 50 percent within the circle, so the density estimate is approximate. Note that the half mile is not about walking distance. A buffer analysis of walking distance using the road network is more accurate, although the gain in accuracy may not be meaningful for policy purposes. The half-mile circle actually approximates a bit over half a mile walking because the route follows streets, not a straight line. The circle is used identify block groups, which also is an approximation given the data available (better than tracts, not as good as building data, which is not easily available.) So the question is if imperfect data can still yield useful results in an efficient use of research time.

Walk distance and density are affected by residential densities and by non-residential uses that may intervene between the neighborhood and the

4 Also: “The use of census tracts for defining catchment areas posed problems for studying walk-on trips in suburban areas where census tracts can be large, often with dimensions well beyond the one-quarter- to one-half-mile distance normally considered to be the maximum distance Americans will walk. In more urbanized areas, especially downtown San Francisco, census tracts (sometimes as small as four or five city blocks) are more suitable for studying the catchments for pedestrian access trips.” They also report (p. 51) “Data on residential densities were obtained from STF 3-A for 1990 census tracts and block groups that most closely corresponded to a half-mile radius surrounding each station.”

Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).Pleasant Hill Station Area Catchment, Cervero et al. (1995).

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station. Non-neighborhood uses, like parking lots, offices, and institutions, have the effect of diluting the apparent density of the neighborhood as it relates to access to transit.

We looked at Google maps to determine land uses within the block groups. If most pedestrians had to walk through a mostly non-residential block group to reach a stations, it affected the functional density of the station area and we kept them in the density estimate. By contrast, a few mostly non-residential block groups did not affect common walking routes to the station, so we did not include them in the density estimate. We excluded, for example, the University of California campus near the Downtown Berkeley Station, Glen Canyon Park near Glen Park Station, the Westfield Shopping Center near Powell Station, and the City College of San Francisco campus near Balboa Park Station.

The station area maps below show block group densities in color in the half-mile circle around the sixteen selected BART stations. Table 3 has station data and Table 4 has the block group data for the maps. We then look for correlations among residential density, number of walk accesses, and walking distance by station.

Findings

Walk distanceIn 2008, 31 percent of BART riders walked to stations, with a median

distance of 0.540 miles. The longest average walk was 1.53 miles to reach the Dublin/Pleasanton Station, but with a small sample of 19 respondents. (The Dublin/Pleasanton median was 1.36 miles and the standard deviation was 0.58 miles.) The shortest average walk was 0.456 miles to reach 16th St. Mission Station, with a large sample size of 405 respondents. (The median was 0.395 miles and the standard deviation was 0.58 miles.) The BART data can be compared to other findings from other surveys.

Comparing this BART data to other survey results shows that BART’s findings are inconsistent with some, usually older, research showing walks of a quarter to half a mile, and consistent with several recent papers showing over half a mile. Table 1 shows all the studies we could find and get access to on the web that reported survey result on walks from home to rapid transit. Table 1: Survey results, home to rapid transit walk distances

Source Placesample

size type of railmean

distancemedian distance

longest walks, % of population Distance

Agrawal, Schlossberg, and Irvin, 2008

3 stations in Portland, 2 in SF Bay Area 328 LRT 0.52 0.47 75th percentile 0.68

Alshalalfah, B. and Shalaby, A., 2007

Toronto subway 0.22 20 percent over .31North Toronto subway 0.28Etobicoke subway 0.25

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Bergman, Gliebe, and Strathman, 2011 Portland

WES commuter rail

0.54 incl. wait 85th percentile 3.00

Burke and Brown, 2007 Brisbane 10,931 train 0.65 0.55 85th percentile 0.98Crowley, Shalaby, and Zarei, 2009

North York City Center 5,090 subway over .25 12% of sample over 1.00

Cervero et al., 1995 BART 90th percentile 1.51Daniels and Mulley, 2011 Sydney 667 trips train 0.5 0.47 2.4 percent over 1.20 Ditto 75th percentile 0.63

El-Geneidy et al., 2014 Montrealcommuter

train 0.51 0.49 85th percentile 0.78 Ditto ditto metro subway 0.35 0.33 85th percentile 0.54Ker and Ginn, 2003 Perth suburban rail 55th percentile 0.62O’Sullivan and Morrall, 2007 Calgary 1,800 LRT 0.4 75th percentile 0.52

Stringham, 1982 Toronto and Edmonton

suburban rapid transit

"well over" 90th percentile 0.28

TCRP, 1996 Chicago Metra 0.75Lewis and Roller [unpublished] SF Bay Area 5,974 BART 0.598 0.54

est. of 84th percentile 0.89

C:\Users\Sherman\Dropbox\Mobility Analysis\BART walk article\Supporting files\Lit matrix.xlsxDrop off with distance

BART provided standard deviations for the distribution of walk access to each station. To estimate the drop off with distance walked, or distance decay, we plotted the station medians minus half a standard deviation, the medians, and the medians plus one standard deviation, from shortest to longest walk, with 100 percent of access for the shortest distance dwindling down to a very small percent for the longest walks. This use of standard deviation is not meant to be statistically valid; it is only for policy estimates of shorter and longer walking distances.

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Figure 1: Distance decay and walk distance from home to BART by Station

Source: BART walk density data.xlsx | decay functionFigure 1 shows the amount of variation by station, different levels of

walk access by distance, and several criteria for planning. Density and walk access

After walk distances, we looked at land use around high-walk-access stations. The maps used Census 2010 accessed through Social Explorer and satellite image from Google maps. The maps show a half-mile radius around each station with block groups shaded for density in persons per acre.

0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 1.100 1.2000%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%Distance decay and walk distance from home to BART by station

Miles walked

Perc

ent w

alki

ng

Median minus one-half the standard deviation

Second order polynomial fit lines

Median Median plus one standard deviation

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Maps 1 and 2: Powell St. and Civic Center Stations, San Francisco

Maps 3 and 4: 16TH Mission and 24TH Mission Stations, San Francisco

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Maps 5 and 6: Glen Park and Balboa Park Stations, San Francisco

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Maps 7 and 8: 12TH Street and 19TH Street Stations, Oakland

Maps 9 and 10: Lake Merritt and Macarthur Stations, Oakland

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Maps 11 and 12: Rockridge Station, Oakland, and Ashby Station, Berkeley

Maps 13 and 14: Downtown Berkeley and North Berkeley Stations

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Maps 15 and 16: El Cerrito Plaza, El Cerrito, and Pleasant Hill

Stations

Table 2 and Figure 2 show the data for the maps above. Density and high walk access were not correlated; the correlation was minus .0885. While stations with many walkers – 24th Street and Mission and 16th Street and Mission – are situated in a dense neighborhood, the results from other stations do not have consistent correlation between density and walk access, or between the walk distance and the numbers of walkers. For example, Glen Park and North Berkeley stations show that people are willing to walk in lower density neighborhoods. At those stations, the lack of parking may be a major explanation for high walk access.

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Table 2: Station data, 16 highest walk access stations

StationPedes-trians

surveyed

People per acre

Median distance walked

Mean distance walked

Standard deviation

Median distance

+ SD

Popu-lation

Block group acres

24th St and Mission

595 59 0.45 0.51 0.321 0.77 20,937 355

Glen Park 446 27 0.43 0.46 0.275 0.71 7,942 292Downtown Berkeley

439 35 0.58 0.62 0.352 0.93 9,076 256

16th St and Mission

405 52 0.40 0.46 0.291 0.69 19,726 378

North Berkeley 333 21 0.58 0.63 0.353 0.92 7,527 363Ashby 330 24 0.49 0.52 0.27 0.76 11,382 47519th St – Oakland

308 25 0.76 0.73 0.316 1.07 7,818 314

El Cerrito Plaza 299 15 0.56 0.57 0.344 0.89 5,881 398Rockridge 290 18 0.49 0.52 0.325 0.81 9,344 433MacArthur 220 18 0.48 0.55 0.326 0.81 9,585 544Lake Merritt 211 28 0.50 0.59 0.367 0.87 10,159 361Civic Center 164 107 0.53 0.59 0.364 0.89 26,648 249Balboa Park 161 31 0.54 0.63 0.353 0.89 9,039 29112th St – Oakland

149 30 0.38 0.48 0.411 0.79 12,148 402

Pleasant Hill 104 14 0.40 0.49 0.338 0.73 8,293 600Powell 101 85 0.56 0.64 0.397 0.95 20,887 246

Total 4,555 33.21 0.505 0.554 195,574 5,889BART walk density data.xlsx | BART walk top 16

Information on density around stations from other studies is limited. Cervero et al. (1995) grouped several stations together based on cluster analysis, so there is, for example, no information on Pleasant Hill itself. They also used census tracts and 90 percent of walk access, leading to finding low density. Pleasant Hill was in their “Suburban Center” group, which had a density of 6 persons per acre. Our research using block groups for Pleasant Hill found a density of 14 per acre. This disparity may also be due to outdated information in Cervero et al.

The lack of correlation of density to walk access was also caused by three large CBD stations. The two BART stations with the highest densities – Powell and Civic Center – had very few pedestrians going to BART and a longer than average walk distance. Large, dense populations near transit do not guarantee that many people will walk to transit.

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Yet another example is 19th Street Oakland and 12th Street Oakland. At 19th Street Oakland the median walking distance is a long 0.76 of a mile. On the other hand, at 12th Street Oakland, just a few blocks away and with a similar density, survey respondents walked a very short median distance, 0.38 of a mile. The 19th Street Oakland Station had the 7th highest walk access while 12th Street ranked much lower, 13th highest and about half as many in number. Figure 2: Walk Access by Residential Density

Civic C

enter

Powell

24th

St and

Miss

ion

16th

St and

Miss

ion

Downto

wn Berke

ley

Balboa

Park

12th

St - Oakla

nd

Lake

Merri

tt

Glen P

ark

19th

St - Oakla

ndAsh

by

North

Berke

ley

Rockri

dge

MacArth

ur

El Cer

rito P

laza

Pleasa

nt Hill

0

100

200

300

400

500

600

700

0

20

40

60

80

100

120

164

101

595

405439

161 149

211

446

308330 333

290

220

299

104

107

85

59

52

3531.061855670103130 28 27 25 24

2118 18

15 14

Walk access by residential density

Pedestrians surveyed

People per acre

BART Stations

Num

ber o

f wal

k ac

cess

resp

onde

nts

Pers

ons p

er a

cre,

blo

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roup

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stati

ons

Much of the lack of correlation is due to these three outliers, Civic Center, Powell, and 12th Street Oakland, all CBDs. The low correlation is probably due to the fact that many people are already close where they need to go and thus do not need transit. These three stations serve the largest employment centers in the Bay Area, so many nearby residents are likely to walk to work and don’t need BART. Figure 3 removes these outliers. Without Civic Center, Powell, and 12th Street Oakland, there is a correlation of 0.709 between density and walk access.

Another clue is time of entry, with high entries in the morning indicating people coming from home, and high entries in the afternoon indicating

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people leaving work to go home. All three downtown stations had about 70 percent of entries being for the home-bound trip.

Figure 3: Walk Access by Residential Density Revised

24th

St - Miss

ion

16th

St - Miss

ion

Dntn B

erke

ley

Balboa

Park

Lake

Merri

tt

Glen P

ark

19th

St Oak

land

Ashby

North

Berke

ley

Rockri

dge

MacArth

ur

El Cer

rito P

laza

Pleasa

nt Hill

0

100

200

300

400

500

600

700

0

10

20

30

40

50

60

70

595

405439

161

211

446

308330 333

290

220

299

104

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52

35

3128 27

25 2421

18 1815 14

Walk access by residential density revised

Pedestrians surveyedPeople per acre

BART Stations

Num

ber o

f wal

k ac

cess

resp

onde

nts

Pers

ons p

er a

cre,

blo

ck g

roup

s clo

se to

stati

on

Density and Non-auto Journeys to WorkIf walk to transit is only one kind of trip related to density, the

relationship of density to all non-auto modes for all trip purposes might be stronger. Such data is not available, but the census does have block group data on mode of journey to work. American Fact Finder gave access to this data better than Social Explorer. Non-auto modes of transit, bicycle, and

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walk were combined and calculated as a percent of non-auto modes plus car modes. The percent of non-auto modes was plotted against density to see what the correlation might be. Figure 4 shows the result. Figure 4: Density and Non-Auto Journey to Work

Powell

Civic C

enter

Dntn Berkeley

16th St-Miss

ion

24th St-Miss

ion

19th Oakla

nd

12th Oakla

ndAsh

by

Balboa Park

Lake M

erritt

N. Berke

ley

El Cerri

to Plaza

Glen Park

Rockridge

Macarth

ur

Pleasant H

ill10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

110.0

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

Population Density and Non-Auto Journey to Work

densityPolynomial (density)% non-auto of non-auto + autoPolynomial (% non-auto of non-auto + auto)

Popu

latio

n de

nsity

of b

lock

gro

ups c

lose

to B

ART

stati

ons

Perc

ent o

f non

-aut

o m

odes

in jo

urne

ys to

wor

k

16 highest walk-in BART Stations

The correlation is 0.847 for the 16 stations, an improvement over using walk access to BART minus three CBD stations and, in fact, a high correlation. It is interesting to get a good correlation with density data alone despite lumpy geography and a margin of error of about 33% in block group populations. More refined statistical analysis with more variables would produce a stronger correlation.

Planning Guidelines

Planning guidelines consider empirical data about walk distances and policy ideas about how to improve transit ridership. Empirical data showing short walks favor policy in response, such as closer transit stops to reduce walk distances or increased density within one-fourth mile to increase riders. Empirical data showing longer walks favor policy affecting a larger land area for densification and improving the walk experience.

The issue of catchment areas for transit overlaps with the general issue of how far people are willing to walk in general. In this context, it is

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important to distinguish the trip to work from other kinds of trips. Commuters are willing to spend more time to get to work than for any other common travel purpose, and walk time is part of that time. Walking to rapid transit supports more work trips than other kinds of walking, such as for routine shopping, services, socializing and recreation such as characterized by the Walk Score. In delineating neighborhoods, Leinberger and Lynch (2015) said “a single walkable place tends not to exceed 600 acres, based upon experience and the limitations people are willing to walk, generally agreed to be between 1500 and 3000 feet.” Those distances are .28 and .57 miles respectively, and may be accurate for general walking while missing the longer walks to transit which are perhaps about 40% of walk accesses.

There is no agreement about how to define the walk access catchment area. Including all riders produces an impractically large area with few riders coming from the outer distances. Some outer limit between 75 and 95 percent of riders would be helpful, without implying it as a strict planning guideline. BART, for example, had a few people walking over two miles to reach a station, outliers are not useful for understanding walk access for planning purposes. Cervero et al (1995) say “The use of the 90 percent rank to demarcate catchment areas was chosen to represent the distances at which the vast majority of access trips are drawn. … Beyond 90 percent, most access trips fall toward the extreme tail of the distance distribution.”

Another issue is what proportion of people to plan for. Crowley, Shalaby, and Zarei (x) ask, “How far will most transit users and potential transit users walk to access transit from (to) their homes and to (from) their workplaces, schools and other non-residential locations?” “There has been a general recognition that for a transit service to be accessible, the planning area for a TOD should extend to between a quarter-mile (400 m) and a half-mile (800 m) from a transit station, roughly the distance associated with a leisurely 5- to 15- minute walk.” It makes sense to plan for most users, but does it make sense to plan for more than most? Planning for over half leaves out almost half of potential riders, but planning for all potential riders would not be cost effective.

The literature has articles supporting longer and shorter guidelines. Daniels and Mulley (x p. 5) say, “A consistent finding of walking distance research including Agrawal et al. (2008) in California and Oregon, Alshalalfah and Shalaby (2007) in Toronto Canada, and Ker and Ginn (2003) to access transit in Perth, Australia is that people walk considerably further to access public transport than commonly assumed “rules of thumb”. This finding has implications for both transport and land use planning, including transit oriented developments (Canepa 2007). People also walk further than assumed for purposes other than access to public transport (Iacono et al. 2008, Larsen et al. 2010).”

Some of the differences over a guideline distance relate to the question being asked. “What is most desirable?” yields a quarter of a mile. How far are people willing to walk so we can plan for more people in a larger area?

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Yields half a mile or more. Distance decay is a measure of attractiveness of a short walk diminishing to willingness over distance.

We wanted some idea of a maximum walk distance for a larger percent of riders, well over 50 percent but less than 100 percent. The application of guidelines should be influenced by cost-effectiveness, which varies among places. A guideline greater than half of potential users should not be rigid, but should encourage planners to look at areas farther than a quarter mile or half mile from a station.

The BART research found a median walking distance of over a half mile. In a normal distribution the range from zero to the median plus one standard deviation includes 84 percent of the population, so planning for that number would reach more people than planning for half, assuming cost-effectiveness. The BART walk access distribution is not a normal curve, but rather skewed to the right. We can still, however, make a rough estimate that planning for about 84 percent of walk access would allow planning out to 0.89 miles from the station, a longer distance than is commonly accepted.

Crowley, Shalaby, and Zarei (x) also say, “Maximizing subway ridership requires that development be more concentrated within a convenient walking distance of transit (within 400m preferably). The strong subway use was observed not only for the AM peak period but throughout the day as well.” While “requires” is too strong a term, the fact that more ridership is generated within a quarter mile is a valid point. Planning guidelines can indicate where to look for opportunities, with decisions guided by cost-effectiveness. Close-in densification seems sure to work, while farther out would need more design support to have attractive walking and discourage autos.

Conclusions and further research

BART data show that the average rider walks more or less half a mile to a station in all sorts of neighborhoods, ranging from CBDs to dense neighborhoods like the Mission District to sprawling suburban areas like Pleasant Hill. The data also show wide range of walk distances. This differs from the five-minute rule described by Moran (2013), confirms Bergman, Gliebe, and Strathman (2011), and challenges pessimistic assumptions about willingness to walk to get to a transit station.

High density around a station supports, but does not correlate with, increased walk access to transit and short walk distances. We found too many anomalous stations – with low density and high walk access, or vice versa – to support a positive relationship. Residential density needs to be combined with other factors to explain the walk to transit.

Further research which would leave walk-to-BART to one side and look at density in relation to all non-auto modes combined, including other transit modes, walk, and bike, and how a rapid transit station could increase the length and amount of walking. More research could look at all the BART stations.

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Factors besides density influence mode choice to reach transit, many of which are included in mode choice computer models: parking availability, parking cost, tolls, car costs, transit costs, total travel time, total travel cost, auto availability, and many more. Over the last few years, BART has implemented parking charges at all stations, which could be studied to see the impact on ridership and on mode of access to stations. For example, very high parking charges at West Oakland BART have not discouraged ridership there, but may not have led to more non-auto access to the station. West Oakland avoids the congestion of driving into San Francisco, the bridge toll, and parking costs, and has very frequent trains, but the station area has low density and unattractive walking.

While barriers and lack of safety discourage walking, we don’t know how much a pleasant, safe walking environment might encourage it. Increased safety, pleasant street lighting, pedestrian separation from traffic, and commercial activity with people on the street could make a difference, making distances over half a mile quite feasible.

References

BART. (2008). BART Station Profile Study [Online]. http://www.bart.gov/sites/default/files/docs/2008StationProfileReport_web.pdf [Accessed: 25th February 2015.] Also email from BART planning staff.

Agrawal, A., Schlossberg, M., & Irvin, K. (2008). How far, by which route and why? A spatial analysis of pedestrian preference, Journal of Urban Design, 13 (1), 81-98. http://darkwing.uoregon.edu/~schlossb/articles/weinstein_howfar.pdf . [Schlossberg et al. & Weinstein et al. are earlier versions of the same paper.]

[Schlossberg, M., Agrawal, A., Irvin, K., & Bekkouche, V. (2007). How far, by which route, and why? A spatial analysis of pedestrian preference. MTI Report, 06-06. San José, CA: Mineta Transportation Institute & College of Business, San José State University.]

[Weinstein, A.1; Bekkouche, V., Irvin, K., & Schlossberg, M. (2006). How Far, by Which Route, and Why? A Spatial Analysis of Pedestrian Preference. TRB 2007 Annual Meeting CD-ROM. http://www.reconnectingamerica.org/assets/Uploads/bestpractice192.pdf . ]

Alshalalfah, B., & Shalaby, A. (2007). Case Study: Relationship of Walk Access Distance to Transit with Service, Travel and Personal Characteristics. Journal of Urban Planning and Development, pp. 114-118. http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9488%282007%29133:2%28114%29.

Bergman, Å, John Gliebe, J., & Strathman, J. (2011). Modeling Access Mode Choice for Inter-Suburban Commuter Rail. Journal of Public Transportation, 14 (4): 23-42, 2011.

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http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1137&context=jpt .

Burke, M. & Brown, A. (2007). Distances people walk for transport. Road and Transport Research, 16 (3), 16-29.

Crowley, D., Shalaby, A., & Zarei, H. (2009). Access Walking Distance, Transit Use, and Transit-Oriented Development in North York City Center, Toronto, Canada. Transportation Research Record: Journal of the Transportation Research Board, pp. 96-105.

Cervero, R., Round, A., Goldman, T., & Kang-Li Wu, K. (1995). Rail Access Modes and Catchment Areas for the BART System Institute of Urban and Regional Development University of California Berkeley, Working Paper UCTC, No. 307, Berkeley: University of California.

Cervero, R. (1993). Ridership Impacts of Transit Focused Development in California. Working Paper UCTC, No 176, Berkeley: University of California.

Daniels, R., & Corinne, M. (2011). “Explaining walking distance to public transport: the dominance of public transport supply,” World Symposium on Transport and Land Use Research, Whistler Canada.

El-Geneidy, A., Grimsrud, M., Wasfi, R., Tétreault, P., & Surprenant-Legault, J. (2014). New evidence on walking distances to transit stops: Identifying redundancies and gaps using variable service areas. Transportation, 41(1), 193-210. http://tram.mcgill.ca/Research/Publications/Transit_service_area.pdf

Ker, I., & S. Ginn. (2003). Myths and Realities in Walkable Catchments: The Case of Walking and Transit. Road and Transport Research. Australian Road Research Board ARRB Group Limited.

Leinberger, Christopher & Patrick Lynch. (2015). The WalkUP Wake-Up Call: Boston. George Washington University School of Business. http://www.smartgrowthamerica.org/documents/walkup-wake-up-call-boston.pdf .

Moran, Maarit Marita. (2013). Walking the Walk : An Assessment of the 5-Minute Rule in Transit Planning. http://repositories.lib.utexas.edu/handle/2152/22683.

O’Sullivan, S. & J. Morrall. (1996). Walking Distances To and From Light-Rail Transit Stations. Transportation Research Record, No. 1538, pp. 19-26. http://trrjournalonline.trb.org/doi/abs/10.3141/1538-03?journalCode=trr .

Stringham, M. (1982). “Travel Behavior Associated With Land Uses Adjacent To Rapid Transit Stations.” ITE Journal 52(4) pp. 18–22.

TCRP. (1996). TCRP Report 16: Transit and Urban Form, Volume 1. Part I: Transit, Urban Form, and the Built Environment: A Summary of Knowledge. Transportation Research Board, National Research Council, Washington, D.C. 1Two reports prepared for this project but not published are Mode of Access and Catchment Areas for Rail Transit, and Influence of Land Use Mix and Neighborhood Design on Transit Demand.

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Untermann, Richard. (1984). Accommodating the Pedestrian: Adapting Towns and Neighborhoods for Walking and Biking. Van Nostrand Reinhold.

Data Appendix

Supporting files are in a Dropbox folder athttps://www.dropbox.com/sh/a0ogonhzvmuvmtw/AAActVNktG8rzAJuJCMZERJRa?dl=0 (Sign in is not really required.)

Table 3: BART 2008 data on walk access from home originsUnweighted sample size correlates to level of walk access

BART 2008 Station Profile Study

StationUnweigh

ted sample

size

Miles from home to BART Standard

DeviationMedian Mean

24th Street Mission 595 .446 .507 .321Glen Park 446 .433 .464 .275Downtown Berkeley 439 .583 .627 .35216th Street Mission 405 .395 .456 .291North Berkeley 333 .579 .639 .353Ashby 330 .486 .517 .27019th Street/Oakland 308 .755 .738 .316El Cerrito Plaza 299 .559 .576 .344Rockridge 290 .485 .520 .325MacArthur 220 .483 .546 .326Lake Merritt 211 .500 .591 .367Civic Center 164 .530 .595 .364Balboa Park 161 .538 .627 .35312 Street/Oakland 149 .381 .480 .411Pleasant Hill 104 .395 .486 .338Powell 101 .556 .646 .397Daly City 96 .493 .629 .380Fremont 92 .765 .802 .405Hayward 87 .634 .716 .479El Cerrito del Norte 85 .581 .647 .409San Leandro 83 .602 .652 .385Montgomery 79 .562 .682 .463Richmond 79 .699 .721 .430

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Fruitvale 76 .735 .826 .503West Oakland 75 .445 .488 .303Union City 73 .735 .768 .310Walnut Creek 54 .641 .733 .430Lafayette 54 .481 .581 .427Concord 54 .697 .737 .377Castro Valley 51 .724 .831 .402San Bruno 50 .744 .868 .409Colma 48 .625 .648 .313Bay Fair 46 .612 .755 .379SSF 42 .524 .749 .525South Hayward 40 .668 .654 .515Embarcadero 36 .478 .647 .518Millbrae 27 .755 .882 .411Coliseum 26 .671 .964 .699Pittsburg/ Bay Point 23 1.002 1.077 .511Dublin/ Pleasanton 19 1.364 1.537 .581North Concord/ Martinez

18 .876 .806 .305

Orinda 6 .610 .747 .467Summary 5974 0.540 .598 .349Mean plus std deviation

.889

Each station is weighted by its sample size for an accurate system-wide figure.Table 4: Census 2010 residential density within 0.5 miles of BART stations

StationsBlock groups within

0.5 mile Population

Block group acres

People per acre

People per

square mile

12th St - Oakland BG 1, CT 4030 1,569 36.2 43.3 27,701BG 1, CT 4031 2,238 85.7 26.1 16,712BG 2, CT 4028 1,917 76.1 25.2 16,127BG 1, CT 4028 1,428 22.4 63.9 40,870BG 4, CT 4034 994 9.4 106.0 67,862BG 1, CT 4029 1,434 96.2 14.9 9,539BG 2, CT 4034 1,349 24.5 55.0 35,214BG 2, CT 4030 1,219 51.1 23.9 15,281

Station summary: 12,148 401.6 30.3

16th St and Mission BG 2, CT 201 1,330 23.1 57.7 36,923BG 3, CT 201 1,482 17.1 86.8 55,531BG 4, CT 201 2,065 21.2 97.4 62,358

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BG 1, CT 208 1,400 16.7 83.7 53,540BG 2, CT 208 1,937 21.4 90.6 58,010BG 1, CT 207 2,295 34.4 66.7 42,715BG 3, CT 202 2,522 34.8 72.5 46,407BG 2, CT 202 2,577 29.2 88.1 56,401BG 1, CT 202 1,170 19.6 59.7 38,236BG 1, CT 201 1,295 48.8 26.5 16,978BG 2, CT 177 1,653 111.7 14.8 9,469

Station summary: 19,726 378.0 52.2

StationsBlock groups within

0.5 mile Population

Block group acres

People per acre

People per

square mile

24th St and Mission BG 1, CT 209 1,784 25.4 70.2 44,910BG 4, CT 209 1,050 25.9 40.6 25,993BG 3, CT 209 595 12.9 46.2 29,599BG 4, CT 253 1,569 36.4 43.1 27,599BG 4, CT 210 853 16.9 50.4 32,269BG 3, CT 210 1,349 25.9 52.0 33,286BG 2, CT 210 983 17.1 57.3 36,685BG 1, CT 210 1,011 17.2 58.8 37,628BG 2, CT 207 1,999 34.4 58.1 37,176BG 3, CT 208 1,274 18.4 69.1 44,240BG 4, CT 208 1,966 28.6 68.6 43,933

BG 1, CT 228.03 1,864 34.0 54.9 35,117BG 1, CT 229.01 1,772 25.4 69.7 44,616BG 2, CT 229.01 1,694 22.5 75.2 48,128BG 3, CT 229.01 1,174 13.6 86.1 55,109

Station summary: 20,937 354.8 59.0

Ashby BG 2, CT 4239.01 1,106 63.4 17.4 11,158BG 3, CT 4005 687 22.7 30.3 19,411

BG 1 CT 4239.01 914 34.2 26.7 17,113BG 3, CT 4235 1,064 45.3 23.5 15,024BG 2, CT 4235 856 49.0 17.5 11,190BG 1, CT 4235 1,198 62.7 19.1 12,229BG 3, CT 4234 1,631 61.3 26.6 17,036BG 4, CT 4234 1,295 49.5 26.2 16,759

BG 1, CT 4240.01 884 34.8 25.4 16,265BG 2, CT 4240.01 680 17.9 38.0 24,296BG 3, CT 4240.01 1,067 34.0 31.4 20,079

Station summary: 11,382 474.7 24.0

Glen Park BG 2, CT 217 1,111 43.3 25.6 16,405BG 4, CT 218 973 34.7 28.0 17,929

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BG 1, CT 311 1,007 38.4 26.2 16,786BG 3, CT 217 1,623 79.9 20.3 13,007BG 3, CT 218 689 20.4 33.8 21,602BG 1, CT 255 1,377 39.1 35.2 22,551BG 2, CT 255 1,162 36.1 32.2 20,610

Station summary: 7,942 291.9 27.2

StationsBlock groups within

0.5 mile Population

Block group acres

People per acre

People per

square mile

Macarthur BG 4, CT 4010 710 34.9 20.3 13,007BG 3, CT 4010 1,260 58.4 21.6 13,818BG 6, CT 4010 955 50.4 18.9 12,123BG 5, CT 4010 1,031 54.8 18.8 12,046BG 1, CT 4014 1,095 58.2 18.8 12,041BG 1, CT 4013 1,041 110.3 9.4 6,038BG 3, CT 4011 1,047 51.8 20.2 12,948BG 2, CT 4011 1,321 45.2 29.2 18,697BG 4, CT 4011 1,125 80.4 14.0 8,955

Station summary: 9,585 544.4 17.6

Pleasant Hill BG 1, CT 3382.03 1,982 205.4 9.6 6,175BG 2, CT 3382.03 1,822 106.6 17.1 10,934BG 2, CT 3400.01 2,309 189.3 12.2 7,807BG 3, CT 3240.01 1,941 56.6 34.3 21,965BG 4, CT 3240.01 239 42.2 5.7 3,628

Station summary: 8,293 600.1 13.8

Rockridge BG 1, CT 4003 1,078 65.7 16.4 10,508BG 2, CT 4002 956 74.0 12.9 8,272BG 4, CT 4003 1,498 73.7 20.3 13,005BG 3, CT 4003 1,091 72.2 15.1 9,673BG 2, CT 4004 1,172 61.0 19.2 12,300BG 3, CT 4004 1,110 48.8 22.7 14,546BG 1, CT 4004 1,421 64.1 22.2 14,195BG 1, CT 4002 1,018 73.1 13.9 8,914

Station summary: 9,344 532.5 17.5

El Cerrito Plaza BG 2, CT 3891 944 114.6 8.2 5,273BG 1, CT 3891 1,058 52.3 20.2 12,954BG 2, CT 3880 1,333 92.3 14.4 9,240BG 2, CT 3901 832 77.2 10.8 6,896BG 2, CT 3892 970 36.7 26.4 16,894BG 1, CT 3892 744 24.4 30.5 19,522

Station summary: 5,881 397.5 14.8

Stations Block groups within Population Block People People

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0.5 milegroup acres

per acre

per square

mileNorth Berkeley BG 1, CT 4222 1,100 76.4 14.4 9,212

BG 3, CT 4222 1,054 51.0 20.7 13,225BG 2, CT 4222 990 39.3 25.2 16,110BG 3, CT 4223 1,101 39.2 28.1 17,995BG 2, CT 4223 1,403 59.8 23.5 15,016BG 1, CT 4223 883 39.4 22.4 14,329BG 2, CT 4219 996 57.6 17.3 11,063

Station summary: 7,527 362.8 20.7

Downtown Berkeley BG 2, CT 4229 2,347 95.2 24.6 15,770BG 1, CT 4229 1,989 39.2 50.7 32,433BG 2, CT 4224 898 21.1 42.6 27,285BG 2, CT 4230 1,520 59.7 25.4 16,284BG 3, CT 4228 2,322 40.4 57.5 36,774

Station summary: 9,076 255.7 35.5

19th St - Oakland BG 2, CT 4028 1,917 76.1 25.2 16,127BG 1, CT 4028 1,428 22.4 63.9 40,870BG 1, CT 4029 1,434 96.2 14.9 9,539

BG 1, CT 4035.01 2,000 66.3 30.2 19,312BG 3, CT 4013 1,039 53.0 19.6 12,538

Station summary: 7,818 314.0 24.9

Lake Merritt BG 1, CT 4033 2,420 184.8 13.1 8,381BG 3, CT 4034 884 9.3 95.5 61,092BG 4, CT 4034 994 9.4 106.0 67,862BG 2, CT 4034 1,439 26.2 55.0 35,214BG 2, CT 4030 1,219 51.1 23.9 15,281BG 1, CT 4030 1,569 36.2 43.3 27,701BG 2, CT 4033 1,634 44.0 37.1 23,746

Station summary: 10,159 360.9 28.1

Powell BG 2, CT 117 976 71.7 13.6 8,711BG 2, CT 121 1,108 15.2 72.7 46,533

BG 2, CT 123.02 1,310 15.3 85.9 54,957BG 1, CT 123.01 1,501 18.7 80.2 51,332BG 2, CT 123.01 1,213 3.8 318.7 203,937BG 1, CT 125.02 1,960 7.6 258.8 165,651BG 2, CT 125.02 1,861 7.6 245.9 157,345BG 2, CT 125.01 1,547 14.1 109.8 70,280BG 1, CT 125.01 3,788 19.4 195.0 124,796BG 2, CT 176.01 2,801 27.0 103.6 66,321BG 5, CT 176.01 1,365 19.2 71.1 45,511BG 1, CT 178.01 1,457 26.5 55.0 35,204

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Station summary: 20,887 246.1 84.9

StationsBlock groups within

0.5 mile Population

Block group acres

People per acre

People per

square mile

Civic Center BG 2, CT 123.01 1,213 3.8 318.7 203,937BG 1, CT 125.02 1,960 7.6 258.8 165,651BG 2, CT 125.02 1,861 7.6 245.9 157,345BG 1, CT 125.01 3,788 19.4 195.0 124,796BG 5, CT 176.01 1,365 19.2 71.1 45,511BG 2, CT 176.01 2,801 27.0 103.6 66,321BG 2, CT 122.01 1,868 7.5 247.6 158,480BG 2, CT 124.01 3,130 11.4 275.5 176,316BG 1, CT 124.01 1,945 11.4 171.1 109,532BG 1, CT 124.02 1,060 24.4 43.4 27,751BG 3, CT 124.02 1,933 15.5 124.9 79,934BG 2, CT 124.02 981 48.8 20.1 12,867BG 3. CT 176.01 2,743 45.9 59.8 38,283

Station summary: 26,648 249.4 106.8

Balboa Park BG 2, CT 312.02 1,743 45.3 38.5 24,633BG 3, CT 261 1,207 30.2 39.9 25,554BG 4, CT 261 1,315 66.5 19.8 12,648BG 5, CT 255 1,494 34.2 43.7 27,988

BG 1, CT 312.01 2,462 46.8 52.7 33,697Station summary: 8,221 223.0 36.9

Source: Social Explorer; Census 2010; American Fact Finder

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Table 5: Monthly Ridership Report Nov. 2014 weekdays

Exits Entries Exits Entriesadd up 424,763 MT 44,790 MT 42,313 MB 6,638 SO 6,520 EM 44,029 EM 38,536 CN 6,286 CN 6,330 PL 29,888 PL 34,473 WP 6,241 WP 6,164 CC 22,647 CC 25,139 RR 6,223 BF 6,060 12 13,820 24 13,822 SL 6,155 SL 6,052 BK 13,645 12 13,795 BF 6,025 AS 5,892 24 13,214 BK 13,386 HY 5,619 RR 5,779 16 12,909 BP 13,338 AS 5,575 HY 5,631 19 12,476 16 12,955 UC 5,008 UC 5,008 BP 12,281 19 12,551 EP 4,980 EP 4,969 DC 9,993 DC 10,137 NB 4,964 NB 4,790 MA 9,350 MA 9,316 CM 4,569 CM 4,692 EN 9,168 FM 8,924 RM 4,506 RM 4,544 FM 8,971 FV 8,674 SB 4,000 SB 3,900 FV 8,759 EN 8,589 LF 3,866 SS 3,697 CL 8,105 GP 8,523 WD 3,769 LF 3,696 GP 7,943 CL 7,922 SS 3,616 WD 3,684 LM 7,467 LM 7,348 SH 3,245 SH 3,402 ED 7,220 ED 7,315 OR 3,004 CV 2,968 SO 7,074 OW 7,232 CV 2,895 OR 2,873 PH 6,934 PH 7,149 NC 2,731 NC 2,835 OW 6,854 MB 6,976 OA 500 OA 220 WC 6,812 WC 6,642 add down 424,763

Exit stations Entry stations Exit stations con. Entry stations con.

Maps credit: Cheyenne Concepcion, May 2015, using Illustrator with data from US Census Data Center TIGER files and American Community Survey Social Explorer, exported to jpgs.