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Interactions between the built and socio-economic environment and driver demographics: spatial econometric models of car crashes in the Columbus Metropolitan Area

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  • Dongkwan Lee
  • Jean-Michel Guldmann
  • Burkhard von Rabenau

Abstract

This research analyzes car crashes resulting from the interactions between (1) the characteristics of the built and socio-economic environment where the crashes take place and (2) the gender and age of the driver at fault. Crashes are classified in terms of seriousness (fatalities/injuries, property damages only) and driver demographics. Data are drawn for the Central Ohio Region over 2006–2011 from the multiple files of the crash database of the Ohio Department of Public Safety. These data are aggregated over Traffic Analysis Zones (TAZ). Additional data include socio-economic, land-use, public transit, road network, and other locational/physical factors, also specified at the TAZ level. Regression analysis is used to explain the numbers of crashes in each of 12 groups. Three age groups are considered: young (15–24), adult (25–64), and older (65+). Spatial autocorrelation effects are tested and corrected by estimating spatial econometric models. The implications of the results for transportation safety policy are discussed.

Suggested Citation

  • Dongkwan Lee & Jean-Michel Guldmann & Burkhard von Rabenau, 2018. "Interactions between the built and socio-economic environment and driver demographics: spatial econometric models of car crashes in the Columbus Metropolitan Area," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 22(1), pages 17-37, January.
    • Handle: RePEc:taf:rjusxx:v:22:y:2018:i:1:p:17-37
    DOI: 10.1080/12265934.2017.1369452
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    References listed on IDEAS

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    1. Sebert Kuhlmann, A.K. & Brett, J. & Thomas, D. & Sain, S.R., 2009. "Environmental characteristics associated with pedestrian-motor vehicle collisions in Denver, Colorado," American Journal of Public Health, American Public Health Association, vol. 99(9), pages 1632-1637.
    2. Daniel J. Graham & Stephen Glaister, 2003. "Spatial Variation in Road Pedestrian Casualties: The Role of Urban Scale, Density and Land-use Mix," Urban Studies, Urban Studies Journal Limited, vol. 40(8), pages 1591-1607, July.
    3. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
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    Cited by:

    1. Hyunho Chang & Dongjoo Park, 2020. "Potentialities of Vehicle Trajectory Big Data for Monitoring Potentially Fatigued Drivers and Explaining Vehicle Crashes on Motorway Sections," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    2. Dongkwan Lee & Jean-Michel Guldmann & Burkhard von Rabenau, 2023. "Impact of Driver’s Age and Gender, Built Environment, and Road Conditions on Crash Severity: A Logit Modeling Approach," IJERPH, MDPI, vol. 20(3), pages 1-22, January.
    3. Tibor Sipos & Anteneh Afework Mekonnen & Zsombor Szabó, 2021. "Spatial Econometric Analysis of Road Traffic Crashes," Sustainability, MDPI, vol. 13(5), pages 1-16, February.
    4. Dongkwan Lee & Jean-Michel Guldmann & Choongik Choi, 2019. "Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers," Sustainability, MDPI, vol. 11(23), pages 1-13, November.

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