Citation

Li Y, Fan WD, Song L, Liu S. J. Transp. Saf. Secur. 2023; 15(11): 1203-1225.

Copyright

(Copyright © 2023, Southeastern Transportation Center, and Beijing Jiaotong University, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19439962.2022.2164814

PMID

unavailable

Abstract

Pedestrians might face more dangers and sustain severer injuries in crashes than others. Also, the crash data has inherent patterns related to both space and time. Crashes that happened in locations with highly aggregated uptrend patterns should be worth exploring to examine the most recently deteriorative factors affecting pedestrian-injury severities in crashes. Therefore, applying proper modeling approaches is needed to identify the causes of pedestrian-vehicle crashes to improve pedestrian safety. In this study, an emerging hotspot analysis is firstly utilized to identify the most targeted hotspots, followed by a proposed XGBoost model that analyzes the most recently deteriorative factors affecting pedestrian injury severities. The overall accuracy of the best model on the hotspot dataset is 94.49%, which shows a relatively high performance compared to conventional models. Seven factors are identified to increase the likelihood of fatal injury, including "land development: farm, wood and pasture" (FWP), "interstate", "US route", "hit and run", "alcohol-impaired driver" (AID), "urban", and "alcohol-impaired-pedestrian". While for incapacitating injury, there are five significant factors including "work zone", "interstate", "US route", "curved roadway" and "alcohol-impaired-pedestrian". The results of this research could give a solid reference for the identification of contributing factors affecting pedestrian-injury severities to policymakers and researchers.

Language: en

Keywords

crash; emerging hotspots; machine learning; North Carolina; Pedestrian; XGBoost