Predicting pedestrian crash occurrence and injury severity in Texas using tree-based machine learning models

Bo Zhao, Natalia Zuniga-Garcia, Lu Xing and Kara M. Kockelman

Transportation Planning and Technology, 2024, vol. 47, issue 8, 1205-1226

Abstract: This study investigates the frequency and injury severity of pedestrian crashes across Texas using tree-based machine learning models. Ten years of police records are used along with roadway inventory and other sources to map 78,000 + pedestrian crashes over 700,000 road segments. Methods like random forests (RF), gradient boosting (LightGBM and XGBoost), and Bayesian additive regression trees (XBART) are applied and compared. The crash frequency models indicate that highway design variables have significant impacts on crash frequencies. Severity models show how higher speed limits significantly increase the likelihood of pedestrian fatalities and severe injuries, and how intoxication (of drivers or pedestrians) lead to more severe injuries. The 4 specifications perform similarly in predicting crash counts, with LightGBM having much faster computing times. Across the crash-severity models, XBART achieved greater precision values but with significantly higher computating times.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03081060.2023.2216202 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:47:y:2024:i:8:p:1205-1226

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20

DOI: 10.1080/03081060.2023.2216202

Access Statistics for this article

Transportation Planning and Technology is currently edited by Dr. David Gillingwater

More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().