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Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

Author

Listed:
  • Dongkwan Lee

    (Department of Public Administration, Kangwon National University, 1 Gangwondaehak-gil, Seoksa-dong, Chuncheon, Gangwon-do 24341, Korea)

  • Jean-Michel Guldmann

    (Department of City and Regional Planning, Ohio State University, 275 West Woodruff Avenue, Columbus, OH 43210, USA)

  • Choongik Choi

    (Department of Public Administration, Kangwon National University, 1 Gangwondaehak-gil, Seoksa-dong, Chuncheon, Gangwon-do 24341, Korea)

Abstract

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6643-:d:290460
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    References listed on IDEAS

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    Cited by:

    1. Xingpei Yan & Zheng Zhu, 2020. "City-Level China Traffic Safety Analysis via Multi-Output and Clustering-Based Regression Models," Sustainability, MDPI, vol. 12(8), pages 1-13, April.
    2. Jaeheon Choi & Kyuil Lee & Hyunmyung Kim & Sunghi An & Daisik Nam, 2020. "Classification of Inter-Urban Highway Drivers’ Resting Behavior for Advanced Driver-Assistance System Technologies using Vehicle Trajectory Data from Car Navigation Systems," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
    3. Sai Chand & Emily Moylan & S. Travis Waller & Vinayak Dixit, 2020. "Analysis of Vehicle Breakdown Frequency: A Case Study of New South Wales, Australia," Sustainability, MDPI, vol. 12(19), pages 1-14, October.
    4. Paúl Narváez-Villa & Blanca Arenas-Ramírez & José Mira & Francisco Aparicio-Izquierdo, 2021. "Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain," IJERPH, MDPI, vol. 18(16), pages 1-21, August.

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