A Brief Note on Modal Access Across America

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Introduction

The Accessibility Observatory at the University of Minnesota is one of, if not the, leading center for the research and application of accessibility-based transportation system evaluation. They have harnessed the growth of big data in transportation, along with cloud computing resources, to conduct multiple groundbreaking accessibility studies that are unprecedented in scope. As part of a larger analysis utilizing their published data, this brief note combines the results of their 2015 auto and transit access to jobs studies (1, 2, 3) to examine the differences in access to jobs between transit and auto modes across the United States. This is a very modest application of the data sets developed through the extensive work conducted by Andrew Owen and Brendan Murphy at the Accessibility Observatory and David Levinson now at the University of Sydney. Any errors in this white paper are the responsibility of the author, however all of the credit for the extensive and indeed, ground-breaking, original accessibility analysis must go to those at the Accessibility Observatory.

Accessibility, also sometimes referred to as access to destinations, is easily understood in general terms, but it is difficult to precisely define and measure it. (4) One definition is “the ease of reaching goods, services, activities and destinations, which together are called opportunities.” (5) As shown in Figure 1, four factors affect accessibility: mobility, mobility substitutes (e.g., telecommuting), transportation system connectivity (the directness and degree of connectivity of the components of the transportation system) and proximity (which is a function of land use), since physical proximity typically increases accessibility. (6)

Figure 1 – Major Factors Affecting Access

Accessibility is a powerful concept for use in transportation planning, as it does not depend on the mechanisms used to achieve it. This allows alternative approaches to be considered on an equal footing with one another. For example, improved access to jobs may be provided by reducing congestion, growing the transit network, locating housing closer to work opportunities, or increasing the opportunities for telecommuting. As such, accessibility metrics provide a valuable complement to mobility and congestion metrics, such as average travel speed or delay. Pratt and Lomax state that “By using door-to-door travel time as the time measure, accessibility via alternative modes can be put on an essentially equal footing, as long as it is recognized that acceptability varies by mode. It is apparently as close to an ideal measure for multimodal performance analysis as can be achieved from the user perspective.” (7)

Accessibility Metrics

There are many ways to measure accessibility. Handy and Neimeier (8) provide a review of accessibility metrics in the planning field and El-Geneidy and Levinson (9) provide a comprehensive review. The model used in the Accessibility Observatory’s studies (1, 2, 3) is called the cumulative opportunity model.

Cumulative Opportunity:

Ci = ∑ Oj Bj

Ci      Opportunities (jobs in these studies) reachable from zone i

Oj     Opportunities (jobs) at zone j

Bj     A binary value equal to 1 if zone j is reachable within a predetermined time from zone I, otherwise equal to 0

This measures the number of jobs available to the residents of a given small zone (census block in the Accessibility Observatory studies) within a given upper bound on door to door travel time. It has the advantages of being both simple to calculate and easy to understand. The units are number of jobs. However, the function uses a step function: all jobs within the travel time limit are counted equally, regardless of how quickly they can be reached, and jobs just one minute beyond the travel time limit are not counted at all. To address this limitation, the Accessibility Observatory studies looked at time thresholds of 10, 20, 30, 40, 50, and 60 minutes. For each time threshold, they then computed worker-weighted average number of jobs across the city as a whole (e.g., if one zone had 50 workers, another had 100, and a third had 200, then the second zone would have twice the weight of the first zone, and the third zone would have four times the weight of the first zone).

Once a worker-weighted average was computing for entire region for each time slice, a weighted sum of the time slices was computed to obtain an accessibility score for each region. The weighted sum was calculated as: (3)

 

 

 

 

 

The Modal Accessibility Gap (MAG), introduced by Kwok and Yeh (10) is a measure of the difference in accessibility between transit and personal automobiles. The formula for computing the MAG is:

Ap      Transit Accessibility

Ac      Automotive Accessibility

The values for the MAG range between -1 and +1. At zero the two modes provide equal accessibility, a value of 1 would mean that there was no access via private automobiles, while a value of -1 would mean there was zero access via transit.

Comparison of Transit vs. Auto Access to Jobs for Forty-Nine Regions across America

The Accessibility Observatory conducted auto access to jobs analyses for 50 of the largest (by population) metropolitan areas in the US, and conducted transit access to jobs analyses for 49 of them (Memphis, TN could not be included due to the lack of available GTFS-formatted transit schedules for Memphis). As described above, they computed a weighted access score combining the results from multiple isochrones, and the rank order varies depending upon which individual isochrone one examines. Using those scores, Figure 2 plots the MAG for each of the 49 cities, beginning with New York, New York which has the highest MAG score of -0.84, and ending with Riverside, California, which has the lowest score of -0.98. While it is not at all surprising that New York City’s score is significantly better than any other city, and San Francisco is significantly better than all cities except New York, it is important to note how low even New York City’s score is. On this scale, 0 would be equal access and -1.0 represents no transit service. Even New York City scores below -0.8. The ten cities with the best MAG scores are:

  1. New York
  2. San Francisco
  3. Boston
  4. Washington
  5. Chicago
  6. Seattle
  7. Philadelphia
  8. Portland
  9. Pittsburgh
  10. Milwaukee

While the weighted scores and the resulting MAG scores provide a more complete and likely more accurate representation, it is hard to have an intuitive feel for what the scores mean. If one picks a particular isochrones, say, for example, jobs reachable within 60 minutes, one can also simply compute the ratio of jobs reachable by transit versus jobs reachable by auto. Figure 3 shows these ratios for both 30 minute and 60 minute isochrones. The order of the cities is the same MAG score ordering used in Figure 2. Figure 3 clearly illustrates how the results vary by the selection of the isochrone one uses for this approach. In particular, while the 30 minute order is approximately the same as that for the MAG scores that use the combined weighted scores, the 60 minute isochrones ratios show dramatically different results.

In order to further investigate the occasionally striking differences in the rankings between the overall MAG score, the 30 minute isochrones, and the 60 minute isochrones, we can take a somewhat deeper dive into Salt Lake City and Las Vegas. These cities would rank 2nd and 5th in comparative transit accessibility if one looks only at the 60 minute isochrones ratios, whereas they rank 14th and 37th using the MAG scores and 14th and 42nd using the 30 minute isochrone ratios. When one looks at the ratio between jobs accessible by auto within 30 minutes vs. 60 minutes, the average across all 49 cities is 51%. However that ratio is highest (97%) for Las Vegas, and 4th highest (68%) for Salt Lake City. Similarly, when comparing the ration of jobs accessible by transit, one finds that Las Vegas has the largest percentage increase in jobs reachable by transit within 60 versus 30 minute, and Salt Lake City has the 6th largest increase. What is happening in these two cities is that as you go further out from the city center, the number of jobs drops off far more rapidly than it does for the average metropolitan region. Therefore increasing the auto catchment area from a 30 minute to a 60 minute isochrone adds far fewer additional new jobs than in the average city. On the other hand, many of the jobs reachable within 30 minutes by auto but not by transit are reachable by a 60 minute or less transit trip. Therefore the modal access gap decreases significantly for the larger 60 minute catchment areas. In addition to providing a satisfactory explanation for these discrepancies, this is an example of the types of “deeper dives” one can take using the data provided by the Accessibility Observatory.

Conclusions

Even in New York, the most transit-friendly city in terms of transit vs. auto access, a 30 minute transit ride provides access to less than 8% as many jobs as are accessible by auto. The situation is somewhat better when one compares 60 minute trips, as the ratio increases to almost 19%. Even so, access to an automobile provides the average New Yorker with the ability to reach far more potential jobs than does transit. In many of the largest U.S. cities, transit provides the average resident with access to less than 5% of the potential jobs that an auto provides access to. Clearly there are opportunities to expand the utility of the transit systems in the U.S. This involves a combination of increased service, better alignment of service with needs, and with land-use changes to increase access. Doing so will reduce the modal access gap, which is a desirable goal. At the same time, the accessibility gap is so large that it is unrealistic to expect the modal gap to shrink to zero in any U.S. city. Transit can be more competitive, but is not an adequate total replacement for auto access for most urban residents. The only way parity could be achieved would be by putting extremely large regulatory restrictions on auto use, which would like have large negative effects on the regional economy.

Figure 2 – Modal Accessibility Gap for 49 Urban Areas

Figure 3 – Ratios of Jobs Reachable by Transit vs. Auto for 30 and 60 Minute Isochrones

References

  1. Owen, A., Murphy, B., and Levinson, D., Access Across America: Auto 2015, CTS 16-07, Accessibility Observatory, University of Minnesota, http://www.cts.umn.edu/Publications/ResearchReports/pdfdownload.pl?id=2724, September 2016.
  2. Owen, A., Murphy, B., and Levinson, D., Access Across America: Transit 2015, CTS 16-09, Accessibility Observatory, University of Minnesota, www.cts.umn.edu/Publications/ResearchReports/pdfdownload.pl?id=2740, December 2016.
  3. Owen, A., Murphy, B., and Levinson, D., Access Across America: Transit 2015 Methodology, CTS 16-10, Accessibility Observatory, University of Minnesota, www.cts.umn.edu/Publications/ResearchReports/pdfdownload.pl?id=2738, December 2016.
  4. Handy, S. Accessibility- vs. Mobility-Enhancing Strategies for Addressing Automobile Dependence in the U.S., Prepared for the European Conference of Ministers of Transport, www.des.ucdavis.edu/faculty/handy/ECMT_report.pdf, 2002. Accessed 31 January 2017.
  5. Litman, T. Evaluating Accessibility for Transportation Planning, Victoria Transport Policy Institute, 2010.
  6. Litman, T. Accessibility: Evaluating People’s To Reach Desired Goods, Services and Activities, from The Online TDM Encyclopedia, http://www.vtpi.org/tdm/tdm84.htm, Victoria Transport Policy Institute, accessed 31 January 2017.
  7. Pratt, R.H. and T. J. Lomax. Performance Measures for Multimodal Transportation Systems, in Transportation Research Record 1518, TRB, National Research Council, Washington, D.C. 1996.
  8. Handy, S.L. and D.A. Neimeier, Measuring Accessibility: An Exploration of Issues and Alternatives in Environment and Planning A, 29(7), 1997.
  9. El-Geneidy, A.M. and D.M. Levinson, Access to Destinations: Development of Accessibility Measures, Report Number MN/RC-2006-16. Minnesota Department of Transportation, St. Paul, Minnesota, 2006.
  10. Kwok R.C.W. and A.G.O. Yeh. The Use of Modal Accessibility Gap as an Indicator for Sustainable Transport Development in Environment and Planning A 36(5) 921 – 936, 2004.

Appendix: Data Used to Produce the Graphs

Note: The 30 and 60 minute transit and auto jobs numbers are taken from (1, 2). The Auto and transit “scores” were calculated using the formula documented by the same researchers in [3]. The other columns are simple arithmetic calculations on the data.

Area Auto Score Transit Score MAG 30 Minute Transit Jobs 60 Minute Transit Jobs 30 Minute Auto Jobs 60 Minute Auto Jobs Ratio of Jobs Reachable by Transit vs. Autos for 30 Minute Isochrone Ratio of Jobs Reachable by Transit vs. Autos for 60 Minute Isochrone
New York 525315.72 46588.27 -0.83708 204,745 1,221,944 2,630,585 6,506,319 7.78% 18.78%
San Francisco 238794.60 15596.46 -0.87738 71,107 374,615 1,134,881 2,946,891 6.27% 12.71%
Boston 203507.60 10321.82 -0.90346 43,778 271,810 938,582 2,661,083 4.66% 10.21%
Washington 237959.35 11263.46 -0.90961 46,416 328,133 1,157,426 3,087,743 4.01% 10.63%
Chicago 263921.22 12043.20 -0.91272 50,586 328,034 1,277,622 3,510,329 3.96% 9.34%
Seattle 152052.65 6568.96 -0.91717 26,591 178,983 744,695 1,523,327 3.57% 11.75%
Philadelphia 202822.39 7653.31 -0.92728 34,234 193,921 992,362 2,960,701 3.45% 6.55%
Portland 135121.27 4848.44 -0.93072 18,790 145,855 687,220 1,105,569 2.73% 13.19%
Pittsburgh 87152.38 2997.65 -0.93350 13,101 77,906 425,627 1,076,698 3.08% 7.24%
Milwaukee 140785.83 4304.40 -0.94067 17,009 126,147 636,663 1,188,778 2.67% 10.61%
New Orleans 76958.06 2020.84 -0.94883 9,114 43,513 317,668 630,749 2.87% 6.90%
Denver 189450.32 4969.26 -0.94888 18,668 159,153 992,037 1,525,933 1.88% 10.43%
Salt Lake City 150735.78 3923.23 -0.94927 13,970 134,513 645,816 963,767 2.16% 13.96%
Baltimore 168365.04 4177.73 -0.95157 17,669 113,063 795,212 2,549,800 2.22% 4.43%
Buffalo 91489.79 2207.99 -0.95287 8,863 57,688 431,900 602,500 2.05% 9.57%
Minneapolis 196260.45 4472.47 -0.95544 17,043 139,841 1,023,854 1,700,783 1.66% 8.22%
Los Angeles 471467.08 10690.02 -0.95566 39,564 358,984 2,323,105 5,577,313 1.70% 6.44%
Austin 126962.60 2664.14 -0.95890 10,808 76,039 600,751 988,117 1.80% 7.70%
Providence 95155.48 1991.60 -0.95900 8,615 48,280 410,653 1,553,681 2.10% 3.11%
San Antonio 127709.79 2566.90 -0.96059 9,533 84,016 614,300 907,807 1.55% 9.25%
San Diego 167990.34 3341.37 -0.96100 11,999 107,182 809,037 1,408,331 1.48% 7.61%
Cleveland 119803.72 2369.88 -0.96120 8,660 74,609 602,907 1,405,385 1.44% 5.31%
Miami 198441.91 3913.26 -0.96132 14,462 122,624 991,891 1,914,507 1.46% 6.40%
San Jose 255195.80 5030.06 -0.96134 16,739 184,272 1,060,964 2,673,982 1.58% 6.89%
Sacramento 124995.74 2459.49 -0.96141 9,483 71,009 606,135 1,084,079 1.56% 6.55%
Louisville 93005.27 1773.99 -0.96257 6,932 51,278 443,985 711,930 1.56% 7.20%
Hartford 123350.80 2253.02 -0.96412 10,091 55,364 588,640 1,520,727 1.71% 3.64%
Columbus 135363.36 2344.24 -0.96595 9,812 64,154 647,442 1,078,674 1.52% 5.95%
Richmond 85741.44 1482.40 -0.96601 6,679 32,582 413,263 702,615 1.62% 4.64%
St. Louis 125519.60 1997.38 -0.96867 7,284 63,333 653,446 1,176,161 1.11% 5.38%
Nashville 79409.20 1195.99 -0.97032 5,027 30,689 379,632 828,851 1.32% 3.70%
Houston 225082.08 3348.40 -0.97068 12,666 106,955 1,150,184 2,453,742 1.10% 4.36%
Charlotte 109424.02 1605.89 -0.97107 6,179 46,654 562,123 1,107,895 1.10% 4.21%
Jacksonville 80282.59 1176.85 -0.97111 4,299 35,635 394,317 638,272 1.09% 5.58%
Indianapolis 119733.76 1739.03 -0.97137 6,790 50,708 619,249 1,101,798 1.10% 4.60%
Las Vegas 167927.09 2409.65 -0.97171 7,469 94,883 768,405 791,240 0.97% 11.99%
Kansas City 114950.64 1641.18 -0.97185 6,851 42,695 608,689 990,808 1.13% 4.31%
Oklahoma City 86678.07 1225.74 -0.97211 4,794 34,679 413,861 606,913 1.16% 5.71%
Virginia Beach 80830.81 1126.80 -0.97250 4,433 31,913 381,616 672,709 1.16% 4.74%
Phoenix 192031.10 2632.14 -0.97296 9,019 94,360 1,006,102 1,687,626 0.90% 5.59%
Tampa 126527.73 1731.31 -0.97300 6,673 51,745 623,831 1,403,980 1.07% 3.69%
Cincinnati 112935.37 1482.95 -0.97408 5,809 42,573 589,391 1,203,539 0.99% 3.54%
Atlanta 158820.51 1925.32 -0.97605 6,869 63,956 804,812 2,046,662 0.85% 3.12%
Dallas 258742.21 2797.28 -0.97861 9,825 95,130 1,346,253 2,878,685 0.73% 3.30%
Birmingham 62777.06 649.05 -0.97953 2,553 17,365 298,483 591,188 0.86% 2.94%
Raleigh 115540.82 1161.83 -0.98009 4,528 33,500 566,967 1,062,914 0.80% 3.15%
Orlando 137113.80 1284.50 -0.98144 4,716 40,633 700,380 1,371,852 0.67% 2.96%
Detroit 190620.85 1707.06 -0.98225 6,020 58,067 988,497 2,021,310 0.61% 2.87%
Riverside 130226.18 1120.87 -0.98293 4,238 34,910 583,025 2,378,179 0.73% 1.47%