Exploring the importance of detailed environment variables in neighborhood commute mode share models

Robert James Schneider, Lingqian Hu, Joseph Stefanich

Abstract


This paper analyzes the relationship between detailed neighborhood environment variables and commute mode share using a dataset drawn from across the United States and includes model validation results. Representing one of the first studies of its kind, we use United States journey-to-work data to explore the following questions: 1) Which detailed environment variables have significant associations with the proportion of people in a neighborhood who take public transit, walk, or bicycle to work? 2) Does adding detailed environment variables to existing, nationally available neighborhood variables improve the predictive accuracy of work commute mode share models? We use a set of 120 randomly selected census tracts to estimate fractional multinomial logit models that predict walk, bicycle, transit, and automobile commute mode shares. The Base Model includes a set of nine significant, nationally available variables identified from a previous analysis of 5,000 tracts. We test 18 additional detailed neighborhood environment variables and identify five variables that have significant associations with commute mode share: sidewalk coverage (positive association with transit and walk), proximity to a rail station (positive association with transit), bicycle facility density (positive association with bicycle), freeway presence (negative association with walk), and mixed land use (positive association with transit, walk, and bicycle). While these detailed environment variables add clarity to our understanding of factors that influence travel behavior, our validation analysis using 50 separate census tracts does not provide conclusive evidence that these variables improve model accuracy. Further studies with larger sample sizes are needed to determine the optimal set of variables to include to predict automobile, transit, pedestrian, and bicycle commute mode shares.

Keywords


Commute Mode Share, Neighborhood Environment, Fractional Multinomial Logit

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References


Alliance for Biking and Walking. (2016). Bicycling and walking in the United States: 2016 benchmarking report. Retrieved from http://www.bikewalkalliance.org/storage/documents/reports/2016benchmarkingreport_web.pdf, 2016.

Cao, X., Mokhtarian, P. L., & Handy, S. L. (2009) Examining the impacts of residential self-selection on travel behavior: A focus on empirical findings. Transport Reviews, 29(3), 359–395.

Center for Transportation Excellence. (2016). Transportation ballot measures, election monitoring database. Retrieved from http://www.cfte.org/elections/past

Cervero, R. (2002). Built Environments and mode choice: Toward a normative framework. Transportation Research Part D, 7, 265–284.

Chatman, D. G. (2009). Residential choice, the built environment, and nonwork travel: Evidence using new data and methods. Environment and Planning A, 4, 1072–1089.

Clifton, K. J., Singleton, P. A., Muhs, C. D., & Schneider, R. J. (2016a). Representing pedestrian activity in travel demand models: Framework and application. Journal of Transport Geography, 52, 111–122.

Clifton, K. J., Singleton, P. A., Muhs, C. D., & Schneider, R. J. (2016b). Development of destination choice models for pedestrian travel. Transportation Research Part A, (94) 255–26.

Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association, (76)3, 265–294.

Federal Highway Administration. (2009). National household travel survey. Washington, DC: Federal Highway Administration.

Forsyth, A., Hearst, M., Oakes, J. M., & Schmitz, K. H. (2008). Design and destinations: Factors influencing walking and total physical activity. Urban Studies, 45( 9), 1973–1996.

Forsyth, A., & Krizek, K. J. (2010). Promoting walking and bicycling: Assessing the evidence to assist planners. Built Environment, 36(4), 429–446.

Google, Inc. (2015a) Google Maps. Retrieved from https://www.google.com/maps

Google, Inc. (2015b) Google Earth, Version 7.1.2.2041. Retrieved from https://www.google.com/earth/desktop/

Grembek, O., Bosman, C., Bigham, J. M, Fine, S., Griswold, J. B., Medury, A., … Ragland, D. R. (2014). Pedestrian safety improvement program (Final technical report). Berkeley, CA: University of California Berkeley Safe Transportation Research and Education, California Department of Transportation.

Griswold, J. B., Medury, A., & Schneider, R. J. (2011). Pilot models for estimating bicycle intersection volumes. Transportation Research Record: Journal of the Transportation Research Board, 2247, 1–7.

Hankey, S., Lindsey, G., Wang, X., Borah, J., Hoff, K., Utecht, B., & Xu, Z. (2012). Estimating use of non-motorized infrastructure: Models of bicycle and pedestrian traffic in Minneapolis, MN. Landscape and Urban Planning, 107, 307–316.

Hankey, S., & Lindsey, G. (2016). Facility-demand models of peak-period pedestrian and bicycle traffic: A comparison of fully-specified and reduced-form models. Transportation Research Record: Journal of the Transportation Research Board, 2586, 48–58.

Krizek, K. J. (2003). Neighborhood services, trip purpose, and tour-based travel. Transportation, 30, 387–410.

Kuzmyak, J. R., Walters, J., Bradley, M, & Kockelman, K. M. (2014). Estimating bicycling and walking for planning and project development: A guidebook, National Cooperative Highway Research Program, NCHRP report 770. Retrieved from http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_770.pdf

Levinson, D. (2015). Modernizing American transportation policy, conservative reform network, first edition. Retrieved from http://conservativereform.com/wp-content/uploads/2015/11/Transportation3.pdf, 2015.

McGuckin, N., & Srinivasan, N. (2005). The journey-to-work in the context of daily travel: For the Census Data for Transportation Planning Conference, paper presented at the Census Data for Transportation Planning Conference, May 11–13, Irvine, CA. Retrieved from http://onlinepubs.trb.org/onlinepubs/archive/conferences/2005/censusdata/resource-journey-to-work.pdf

Miranda-Moreno, L. F., & Fernandes, D. (2011). Pedestrian activity modeling at signalized intersections: Land use, urban form, weather and spatiotemporal patterns. Transportation Research Record: Journal of the Transportation Research Board, 2264, 74–82.

Mullahy, J. (2015). Multivariate fractional regression estimation of econometric share models. Journal of Econometric Methods, 4(1) 71–100.

Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11(6), 619–632.

Parkin, J., Wardman, M., & Page, M. (2008). Estimation of the determinants of bicycle mode share for the journey to work using census data. Transportation, 35(1) 93–109.

Ramalho, E. A., Ramalho, J. J., & Murteira, J. M. (2011). Alternative estimating and testing empirical strategies for fractional regression models. Journal of Economic Surveys, 25(1) 19–68.

Saelens, B. E., & Handy, S. L. (2008). Built environment correlates of walking: A review. Medicine and Science in Sports and Exercise, 40(7 supplement 1), S550-S556.

Sallis, J. F., Floyd, M. F., Rodríguez, D. A., & Saelens, B. E. (2012). Role of built environments in physical activity, obesity, and cardiovascular disease. Circulation, 125(5), 729–737.

Schneider, R. J., Henry, T., Mitman, M. F., Stonehill, L., & Koehler, J. (2012). Development and application of the San Francisco pedestrian intersection volume model. Transportation Research Record: Journal of the Transportation Research Board, 2299, 65–78.

Schneider, R. J. (2015). Local environment characteristics associated with walking and taking transit to shopping districts. Journal of Transport and Land Use, 8(2) 125–147.

Schneider, R. J., Shafizadeh, K., & Handy, S. L. (2015). Method to adjust Institute of Transportation Engineers vehicle trip-generation estimates in smart-growth areas. Journal of Transport and Land Use, 8(1), 69–83.

Schneider, R. J., Hu, L., & Stefanich, J. (2017). Development of a neighborhood commute mode share model using nationally available data. Transportation. doi.org/10.1007/s11116-017-9813-z

Shoup, L., & Lang, M. (2011). Transportation 101: An introduction to federal transportation policy, Transportation for America. Retrieved from http://t4america.org/docs/Transportation%20101.pdf

Sivakumar, A., & Bhat, C. (2002). Fractional split-distribution model for statewide commodity-flow analysis. Transportation Research Record: Journal of the Transportation Research Board, 1790, 80–88.

StataCorp. (2007). Stata Statistical Software: Release 10. College Station, TX: StataCorp LP.

Strauss, J., & Miranda-Moreno, L. F. (2013). Spatial modeling of bicycle activity at signalized intersections. Journal of Transport and Land Use, 6(2) 47–58.

United States Census Bureau. (2015). American Community Survey (ACS), methodology webpage. Retrieved from https://www.census.gov/programs-surveys/acs/methodology.html

United States Census Bureau. (2015). Longitudinal Employer-Household Dynamics (LEHD). Data webpage. Retrieved from http://lehd.ces.census.gov/data/

United States Congress. (2015). Fixing America’s surface transportation act. Retrieved from https://www.congress.gov/bill/114th-congress/house-bill/22/text

United States Department of Health and Human Services. (2015). Step it up! The surgeon general’s call to action to promote walking and walkable communities. Retrieved from http://www.surgeongeneral.gov/library/calls/walking-and-walkable-communities/call-to-action-walking-and-walkable-communites.pdf

United States National Oceanic and Atmospheric Administration (NOAA). (2014). National Climatic Data Center, 1981-2010 U.S. climate normals. Retrieved from http://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/climate-normals/1981-2010-normals-data

Voulgaris, C. T., Taylor, B. D., Blumenberg, E., Brown, A., & Ralph, K. (2017). Synergistic neighborhood relationships with travel behavior: An analysis of travel in 30,000 US neighborhoods Journal of Transport and Land Use, 10(1), 437–461.

Wang, Z., & Wolman, A. L. (2014). Payment choice and the future of currency: Insights from two billion retail transactions, white paper. Richmond, VA: Federal Reserve Bank of Richmond, Research Department.




DOI: http://dx.doi.org/10.5198/jtlu.2018.927


Copyright (c) 2018 Robert James Schneider, Lingqian Hu, Joseph Stefanich