Residential accessibility’s relationships with crash rates per capita
Keywords:Accessibility, Safety, Crashes, Travel Behavior, Exposure
This paper examines the relationship between residential accessibility, i.e., accessibility from a person’s home address, and their likelihood of being in a crash over a three-year period. We explore two potential relationships with accessibility. The first is that persons who live in areas with high destination accessibility may drive less and therefore are less likely to be in vehicular crashes. The second is that persons who live in high vehicle miles traveled (VMT) accessibility areas may be exposed to higher levels of traffic in their regular activity space and therefore may be more likely to be in crashes of all modal types. Examining traffic analysis zones in Knoxville, Tennessee, this research finds some evidence for each of these hypothesized effects. These oppositely directed effects have dominant influence within different travel-time thresholds. The first relationship between destination accessibility and fewer crashes is found to be strongest for 10-minute auto accessibility, whereas the second relationship between VMT accessibility and more crashes is found to occur at 10-minute, 20-minute, and 30-minute thresholds.
Abdel-Aty, M., Pande, A., Lee, C., Das, A., Nevarez, A., Darwiche, A., & Devarasetty, P. (2009). Reducing fatalities and severe injuries on Florida’s high-speed multi-lane arterial corridors: Part I, preliminary severity analysis of driver crash involvements, final report, April 2009. Orlando, FL: University of Central Florida, Center for Advanced Transportation Systems Simulation. Retrieved from http://ntl.bts.gov/lib/31000/31500/31520/FDOT_BD548-22_rpt_PART_I.pdf
Ahlfeldt, G. (2011). If Alonso was right: Modeling accessibility and explaining the residential land gradient. Journal of Regional Science, 51, 318–338. doi.org/10.1111/j.1467-9787.2010.00694.x
Boisjoly, G., & El-Geneidy, A. M. (2017). How to get there? A critical assessment of accessibility objectives and indicators in metropolitan transportation plans. Transport Policy, 55, 38–50. doi.org/10.1016/j.tranpol.2016.12.011
Chen, P., & Shen, Q. (2016). Built environment effects on cyclist injury severity in automobile-involved bicycle crashes. Accident Analysis & Prevention, 86, 239–246. doi.org/10.1016/j.aap.2015.11.002
Chicago Metropolitan Agency for Planning. (2008). Preferred regional scenario. Chicago: Chicago Metropolitan Agency for Planning.
Ewing, R., & Cervero, R. (2010). Travel and the built environment — a meta-analysis. Journal of the American Planning Association, 76, 265–294. doi.org/10.1080/01944361003766766
Ewing, R., & Dumbaugh, E. (2009). The built environment and traffic safety: A review of empirical evidence. Journal of Planning Literature, 23(4), 347–367. doi.org/https://doi.org/10.1177/0885412209335553
Ewing, R., Hamidi, S., & Grace, J. B. (2016a). Urban sprawl as a risk factor in motor vehicle crashes. Urban Studies, 53(2), 247–266. doi.org/10.1177/0042098014562331
Ewing, R., Hamidi, S., & Grace, J. B. (2016b). Urban sprawl as a risk factor in motor vehicle crashes. Urban Studies, 53(2), 247–266. doi.org/10.1177/0042098014562331
Geurs, K. T., & Van Wee, B. (2004). Accessibility evaluation of land-use and transport strategies: Review and research directions. Journal of Transport Geography, 12(2), 127–140. doi.org/10.1016/j.jtrangeo.2003.10.005
Handy, S. L., & Niemeier, D. A. (1997). Measuring accessibility: An exploration of issues and alternatives. Environment and Planning A, 29, 1175–1194.
Hansen, W. G. (1959). How accessibility shapes land use. Journal of the American Institute of Planners, 12, 73–76.
Heyman, A., Law, S., & Berghauser Pont, M. (2018). How is location measured in housing valuation? A systematic review of accessibility specifications in hedonic price models. Urban Science, 3(1), 3. doi.org/10.3390/urbansci3010003
Hezaveh, A. M., Arvin, R., & Cherry, C. R. (2019). A geographically weighted regression to estimate the comprehensive cost of traffic crashes at a zonal level. Accident Analysis & Prevention, 131(June), 15–24. doi.org/10.1016/j.aap.2019.05.028
Kim, K., Pant, P., & Yamashita, E. (2010). Accidents and accessibility: Measuring influences of demographic and land-use variables in Honolulu, Hawaii. Transportation Research Record, (2147), 9–17. doi.org/10.3141/2147-02
Lochmueller & Associates, I. (2012). Knoxville regional travel model update 2012 model development and validation report. Evansville, IN: Lochmueller & Associates.
Lucy, W. H. (2003). Mortality risk associated with leaving home: Recognizing the relevance of the built environment. American Journal of Public Health, 93(9), 1564–1569. Retrieved from https://ajph.aphapublications.org/doi/pdfplus/10.2105/AJPH.93.9.1564
Marshall, W. E., & Garrick, N. W. (2011). Does street network design affect traffic safety? Accident Analysis and Prevention, 43(3), 769–781. doi.org/10.1016/j.aap.2010.10.024
Merlin, L. A., Levine, J., & Grengs, J. (2018). Accessibility analysis for transportation projects and plans. Transport Policy, 69, 35–48. doi.org/10.1016/j.tranpol.2018.05.014
Miller, H. J. (1991). Modelling accessibility using space-time prism concepts within geographical information systems. International Journal of Geographic Information Science, 5, 287–301.
Mohamed, R., vom Hofe, R., & Mazumder, S. (2014). Jurisdictional spillover effects of sprawl on injuries and fatalities. Accident Analysis & Prevention, 72, 9–16. doi.org/10.1016/j.aap.2014.05.028
OECD, & ITF. (2016). Road safety annual report 2016. Paris: International Transport Forum. Retrieved from http://dx.doi.org/10.1787/irtad-2016-en
Ortuzar, J. de D., & Willumsen, L. G. (2011). Modeling transport (4th ed.). Chichester, England: John Wiley & Sons.
Owen, A., & Levinson, D. M. (2015). Modeling the commute mode share of transit using continuous accessibility to jobs. Transportation Research Part A: Policy and Practice, 74, 110–122. doi.org/10.1016/j.tra.2015.02.002
Quddus, M. A. (2008). Modelling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data. Accident Analysis & Prevention, 40(4), 1486–1497. doi.org/10.1016/j.aap.2008.03.009
Retting, R. A., Ferguson, S. A., & McCartt, A. T. (2003). A review of evidence-based traffic engineering measures designed to reduce pedestrian-motor vehicle crashes. American Journal of Public Health, 93(9), 1456–1463. doi.org/10.2105/AJPH.93.9.1456
Rothman, L., Buliung, R., Macarthur, C., To, T., & Howard, A. (2014). Walking and child pedestrian injury: A systematic review of built environment correlates of safe walking. Injury Prevention, 20(1), 41–49. doi.org/10.1136/injuryprev-2012-040701
Santos, A., McGuckin, N., Nakamoto, H. Y., Gray, D., & Liss, S. (2011). Summary of travel trends: 2009 National Household Travel Survey. Washington, DC: US Department of Transportaion.
Stevens, M. R. (2016). Does compact development make people drive less? Journal of the American Planning Association, 83(1), 7–18. doi.org/10.1080/01944363.2016.1240044
Stoker, P., Garfinkel-Castro, A. A., Khayesi, M., Odero, W., Mwangi, M. N., Peden, M., & Ewing, R. (2015). Pedestrian safety and the built environment: A review of the risk factors. Journal of Planning Literature, 30(4), 377–392. doi.org/10.1177/0885412215595438
Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica: Journal of the Econometric Society, 26(1), 24–36.
Virginia Department of Transportation. (2017). Smart scale: Finding the right transportation rrojects for Virginia. Retrieved from http://vasmartscale.org/
Waddell, P. (2002). UrbanSim — modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68, 297–314.
Wier, M., Weintraub, J., Humphreys, E. H., Seto, E., & Bhatia, R. (2009). An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. Accident Analysis & Prevention, 41(1), 137–145. doi.org/10.1016/j.aap.2008.10.001
Yeo, J., Park, S., & Jang, K. (2015). Effects of urban sprawl and vehicle miles traveled on traffic fatalities. Traffic Injury Prevention, 16(4), 397–403. doi.org/10.1080/15389588.2014.948616
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Copyright (c) 2020 Louis A Merlin, Chris R Cherry, Amin Mohamadi-Hezaveh, Eric Dumbaugh
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