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Crash analysis at intersections in the CBD: A survival analysis model

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  • Bagloee, Saeed Asadi
  • Asadi, Mohsen

Abstract

Enhancing the safety level of urban roads especially in CBDs is paramount. Due to a large number of intersections in what is usually a grid road system in the CBDs, we investigate crashes occurring in and around an intersection. The question of interest in this study is: does the nature of crashes at intersections differ from those of the roads at midblock? Stated more precisely, considering the intersection as a reference point, does the distance to the reference point (i.e. midblock locations on the roads) correlate with different types of crashes compared to that of the intersection? A right answer can lead traffic engineers and safety auditors to propose different safety measures at intersections and the midblock locations. As a pilot study, we collected the last 9years crash data of the CBD of Melbourne, Australia. For the first time, we employ Survival Analysis models -including Exponential, Weibull, and Log-logistic- to investigate a space-dependent phenomenon (i.e. accidents at proximity to the intersection). Of the outcome, highlights are: (i) police presence at busy intersections during busy night outs and weekends highly improves the pedestrian safety (ii) raised crossings at midblock locations lower likelihood of crashes of pedestrians as well as cars, (iii) lighting conditions at intersections must be watched and kept at a high level. (iv) Severity, likelihood, and location have no known association with the level of congestion. In other words, safety is first, always and everywhere. The results can be of interest to traffic authorities and policy makers in reinforcing traffic calming measures in the cities. The codes developed in this study are made available to the research community to be used in further studies.

Suggested Citation

  • Bagloee, Saeed Asadi & Asadi, Mohsen, 2016. "Crash analysis at intersections in the CBD: A survival analysis model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 558-572.
    • Handle: RePEc:eee:transa:v:94:y:2016:i:c:p:558-572
    DOI: 10.1016/j.tra.2016.10.019
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    References listed on IDEAS

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    1. Dong, Chunjiao & Nambisan, Shashi S. & Richards, Stephen H. & Ma, Zhuanglin, 2015. "Assessment of the effects of highway geometric design features on the frequency of truck involved crashes using bivariate regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 30-41.
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    Cited by:

    1. Binghong Pan & Shasha Luo & Jinfeng Ying & Yang Shao & Shangru Liu & Xiang Li & Jiaqi Lei, 2021. "Evaluation and Analysis of CFI Schemes with Different Length of Displaced Left-Turn Lanes with Entropy Method," Sustainability, MDPI, vol. 13(12), pages 1-27, June.
    2. Zhao, Jing & Yan, Jiachao & Wang, Jiawen, 2019. "Analysis of alternative treatments for left turn bicycles at tandem intersections," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 314-328.
    3. Abdul Rashid Mussah & Yaw Adu-Gyamfi, 2022. "Machine Learning Framework for Real-Time Assessment of Traffic Safety Utilizing Connected Vehicle Data," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    4. Saeed Asadi Bagloee & Majid Sarvi & Avishai Ceder, 2017. "Transit priority lanes in the congested road networks," Public Transport, Springer, vol. 9(3), pages 571-599, October.
    5. Bagloee, Saeed Asadi & Sarvi, Majid & Wolshon, Brian & Dixit, Vinayak, 2017. "Identifying critical disruption scenarios and a global robustness index tailored to real life road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 98(C), pages 60-81.

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