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Analysis of Traffic Crashes Involving Pedestrians Using Big Data: Investigation of Contributing Factors and Identification of Hotspots

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  • Kun Xie
  • Kaan Ozbay
  • Abdullah Kurkcu
  • Hong Yang

Abstract

This study aims to explore the potential of using big data in advancing the pedestrian risk analysis including the investigation of contributing factors and the hotspot identification. Massive amounts of data of Manhattan from a variety of sources were collected, integrated, and processed, including taxi trips, subway turnstile counts, traffic volumes, road network, land use, sociodemographic, and social media data. The whole study area was uniformly split into grid cells as the basic geographical units of analysis. The cell‐structured framework makes it easy to incorporate rich and diversified data into risk analysis. The cost of each crash, weighted by injury severity, was assigned to the cells based on the relative distance to the crash site using a kernel density function. A tobit model was developed to relate grid‐cell‐specific contributing factors to crash costs that are left‐censored at zero. The potential for safety improvement (PSI) that could be obtained by using the actual crash cost minus the cost of “similar” sites estimated by the tobit model was used as a measure to identify and rank pedestrian crash hotspots. The proposed hotspot identification method takes into account two important factors that are generally ignored, i.e., injury severity and effects of exposure indicators. Big data, on the one hand, enable more precise estimation of the effects of risk factors by providing richer data for modeling, and on the other hand, enable large‐scale hotspot identification with higher resolution than conventional methods based on census tracts or traffic analysis zones.

Suggested Citation

  • Kun Xie & Kaan Ozbay & Abdullah Kurkcu & Hong Yang, 2017. "Analysis of Traffic Crashes Involving Pedestrians Using Big Data: Investigation of Contributing Factors and Identification of Hotspots," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1459-1476, August.
    • Handle: RePEc:wly:riskan:v:37:y:2017:i:8:p:1459-1476
    DOI: 10.1111/risa.12785
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    References listed on IDEAS

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    1. Abdel-Aty, Mohamed & Lee, Jaeyoung & Siddiqui, Chowdhury & Choi, Keechoo, 2013. "Geographical unit based analysis in the context of transportation safety planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 62-75.
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    2. Xie, Kun & Ozbay, Kaan & Yang, Di & Xu, Chuan & Yang, Hong, 2021. "Modeling bicycle crash costs using big data: A grid-cell-based Tobit model with random parameters," Journal of Transport Geography, Elsevier, vol. 91(C).
    3. Angelo Rampinelli & Juan Felipe Calderón & Carola A. Blazquez & Karen Sauer-Brand & Nicolás Hamann & José Ignacio Nazif-Munoz, 2022. "Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile," IJERPH, MDPI, vol. 19(17), pages 1-21, September.
    4. Michael Greenberg & Anthony Cox & Vicki Bier & Jim Lambert & Karen Lowrie & Warner North & Michael Siegrist & Felicia Wu, 2020. "Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2113-2127, November.
    5. Mingyu Kang & Anne Vernez Moudon & Haena Kim & Linda Ng Boyle, 2019. "Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS," IJERPH, MDPI, vol. 16(19), pages 1-14, September.
    6. Bao, Jie & Yang, Zhao & Zeng, Weili & Shi, Xiaomeng, 2021. "Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data," Journal of Transport Geography, Elsevier, vol. 94(C).
    7. Tsan‐Ming Choi & James H. Lambert, 2017. "Advances in Risk Analysis with Big Data," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1435-1442, August.
    8. Wu, Peijie & Chen, Tianyi & Diew Wong, Yiik & Meng, Xianghai & Wang, Xueqin & Liu, Wei, 2023. "Exploring key spatio-temporal features of crash risk hot spots on urban road network: A machine learning approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    9. Yi‐Jen (Ian) Ho & Siyuan Liu & Jingchuan Pu & Dian Zhang, 2022. "Is it all about you or your driving? Designing IoT‐enabled risk assessments," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4205-4222, November.
    10. Shen, Hui & Lin, Jane, 2020. "Investigation of crowdshipping delivery trip production with real-world data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    11. Liu, Jinli & Das, Subasish & Zhan, F. Benjamin & Khan, Md Nasim, 2024. "Spatial analysis of geographical disparities in pedestrian safety," Transport Policy, Elsevier, vol. 156(C), pages 164-181.
    12. Yanmin Qi & Zuduo Zheng & Dongyao Jia, 2020. "Exploring the Spatial-Temporal Relationship between Rainfall and Traffic Flow: A Case Study of Brisbane, Australia," Sustainability, MDPI, vol. 12(14), pages 1-24, July.
    13. Kun Xie & Kaan Ozbay & Hong Yang & Di Yang, 2019. "A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1342-1357, June.
    14. Gao, Jingqin & Zuo, Fan & Ozbay, Kaan & Hammami, Omar & Barlas, Murat Ledin, 2022. "A new curb lane monitoring and illegal parking impact estimation approach based on queueing theory and computer vision for cameras with low resolution and low frame rate," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 137-154.
    15. Lei, Yiyuan & Ozbay, Kaan, 2021. "A robust analysis of the impacts of the stay-at-home policy on taxi and Citi Bike usage: A case study of Manhattan," Transport Policy, Elsevier, vol. 110(C), pages 487-498.

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