Older Pedestrian Traffic Crashes Severity Analysis Based on an Emerging Machine Learning XGBoost

Manze Guo, Zhenzhou Yuan, Bruce Janson, Yongxin Peng, Yang Yang and Wencheng Wang
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Manze Guo: Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
Zhenzhou Yuan: Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
Bruce Janson: Department of Civil Engineering, University of Colorado Denver, Denver, CO 80217-3364, USA
Yongxin Peng: Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843-3135, USA
Yang Yang: Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
Wencheng Wang: Beijing Municipal Institute of City Planning & Design, Beijing 100045, China

Sustainability, 2021, vol. 13, issue 2, 1-26

Abstract: Older pedestrians are vulnerable on the streets and at significant risk of injury or death when involved in crashes. Pedestrians’ safety is critical for roadway agencies to consider and improve, especially older pedestrians aged greater than 65 years old. To better protect the older pedestrian group, the factors that contribute to the older crashes need to be analyzed deeply. Traditional modeling approaches such as Logistic models for data analysis may lead to modeling distortions due to the independence assumptions. In this study, Extreme Gradient Boosting (XGBoost), is used to model the classification problem of three different levels of severity of older pedestrian traffic crashes from crash data in Colorado, US. Further, Shapley Additive explanations (SHAP) are implemented to interpret the XGBoost model result and analyze each feature’s importance related to the levels of older pedestrian crashes. The interpretation results show that the driver characteristic, older pedestrian characteristics, and vehicle movement are the most important factors influencing the probability of the three different severity levels. Those results investigate each severity level’s correlation factors, which can inform the department of traffic management and the department of road infrastructure to protect older pedestrians by controlling or managing some of those significant features.

Keywords: older pedestrian traffic safety; pedestrian traffic crashes; machine learning; crashes severity; SHAP; XGBoost (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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