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Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida

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  • Ulak, Mehmet Baran
  • Ozguven, Eren Erman
  • Spainhour, Lisa
  • Vanli, Omer Arda

Abstract

This study attempts to understand the unique nature of crashes involving aging drivers, unlike many previous crash-focused traffic safety studies mostly focusing on the general population. The utmost importance is given to answering the following question: How do the crashes involving aging drivers vary compared to crashes involving other age groups? To achieve this objective, a three-step spatial analysis was conducted using geographic information systems (GIS) with a case study application on three urban counties in the Northwest Florida region, based on crash data obtained from the Florida Department of Transportation (FDOT). First, crash clusters were investigated using a kernel density estimation (KDE) approach. Second, a crash density ratio difference (DRD) measure was proposed for comparing maxima-normalized crash densities for two different age groups. Third, a population factor (PF) was developed in order to investigate effect of spatial dependency by incorporating the effect of both number and percent of 65+ populations in a region. This spatial analysis was followed by a logistic regression-based approach in order to identify the statistically significant factors that can help investigate the distinct patterns of crashes involving aging drivers. Results of this study indicate that crashes involving aging drivers differ from other age group crashes both spatially and temporally. Further, the DRD and PF factors are useful metrics to identify and investigate important regions of study. The GIS-based knowledge gained from this research can contribute to the development of more reliable aging-focused safety plans and models.

Suggested Citation

  • Ulak, Mehmet Baran & Ozguven, Eren Erman & Spainhour, Lisa & Vanli, Omer Arda, 2017. "Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida," Journal of Transport Geography, Elsevier, vol. 58(C), pages 71-91.
    • Handle: RePEc:eee:jotrge:v:58:y:2017:i:c:p:71-91
    DOI: 10.1016/j.jtrangeo.2016.11.011
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    References listed on IDEAS

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    1. Maddala,G. S., 1986. "Limited-Dependent and Qualitative Variables in Econometrics," Cambridge Books, Cambridge University Press, number 9780521338257.
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    4. Xie, Zhixiao & Yan, Jun, 2013. "Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach," Journal of Transport Geography, Elsevier, vol. 31(C), pages 64-71.
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    Cited by:

    1. Huang, Yuan & Wang, Xiaoguang & Patton, David, 2018. "Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach," Journal of Transport Geography, Elsevier, vol. 69(C), pages 221-233.
    2. Mahyar Ghorbanzadeh & Mohammadreza Koloushani & Mehmet Baran Ulak & Eren Erman Ozguven & Reza Arghandeh Jouneghani, 2020. "Statistical and Spatial Analysis of Hurricane-induced Roadway Closures and Power Outages," Energies, MDPI, vol. 13(5), pages 1-18, March.
    3. Kidando, Emmanuel & Moses, Ren & Abdelrazig, Yassir & Ozguven, Eren Erman, 2017. "Safety Analysis Considering the Impact of Travel Time Reliability on Elderly Drivers," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 56(1), April.
    4. Dulebenets, Maxim A. & Abioye, Olumide F. & Ozguven, Eren Erman & Moses, Ren & Boot, Walter R. & Sando, Thobias, 2019. "Development of statistical models for improving efficiency of emergency evacuation in areas with vulnerable population," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 233-249.

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