Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle-bicycle crashes

Seyed Alireza Samerei; Kayvan Aghabayk; Nirajan Shiwakoti; Amin Mohammadi

doi:10.1016/j.jsr.2021.09.005

Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle-bicycle crashes

J Safety Res. 2021 Dec:79:246-256. doi: 10.1016/j.jsr.2021.09.005. Epub 2021 Sep 23.

Authors

Seyed Alireza Samerei¹, Kayvan Aghabayk², Nirajan Shiwakoti³, Amin Mohammadi⁴

Affiliations

¹ School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran. Electronic address: alireza.samerei@ut.ac.ir.
² School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
³ School of Engineering, RMIT University, Melbourne, Australia.
⁴ Mianeh Technical and Engineering Faculty, University of Tabriz, Tabriz, Iran.

PMID: 34848005
DOI: 10.1016/j.jsr.2021.09.005

Abstract

Introduction: In recent years, Australia is seeing an increase in the total number of cyclists. However, the rise of serious injuries and fatalities to cyclists has been a major concern. Understanding the factors affecting the fatalities and injuries of bicyclists in crashes with motor vehicles is important to develop effective policy measures aimed at improving the safety of bicyclists. This study aims to identify the factors affecting motor vehicle-bicycle (MVB) crashes in Victoria, Australia and introducing effective countermeasures for the identified risk factors.

Method: A data set of 14,759 MVB crash records from Victoria, Australia between 2006 and 2019 was analyzed using the binary logit model and latent class clustering.

Results: It was observed that the factors that increase the risk of fatalities and serious injuries of bicyclists (FSI) in all clusters are: elderly bicyclist, not using a helmet, and darkness condition. Likewise, in areas with no traffic control, clear weather, and dry surface condition (cluster 1), high speed limits increase the risk of FSI, but the occurrence of MVB crashes in cross intersection and T-intersection has been significantly associated with a reduction in the risk of FSI. In areas with traffic control and unfavorable weather conditions (cluster 2), wet road surface increases the risk of FSI, but the areas with give way sign and pedestrian crossing signs reduce the risk of FSI. Practical Applications: Recommendations to reduce the risk of fatalities or serious injury to bicyclists are: improvement of road lighting and more exposure of bicyclists using reflective clothing and reflectors, separation of the bicycle and vehicle path in mid blocks especially in high-speed areas, using a more stable bicycle for the older people, monitoring helmet use, improving autonomous emergency braking, and using bicyclist detection technology for vehicles.

Keywords: Bicyclist injury severity; Cycling safety; Latent class clustering and binary logit model; Motor vehicle-bicycle crashes.

MeSH terms

Accidents, Traffic*
Aged
Bicycling* / injuries
Cluster Analysis
Humans
Logistic Models
Motor Vehicles
Victoria / epidemiology