Citation

Zhang G, Xuan Q, Cai Y, Hu X, Yin Y, Li Y. J. Saf. Res. 2024; 89: 262-268.

Copyright

(Copyright © 2024, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2024.04.004

PMID

38858050

Abstract

INTRODUCTION: Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity.

METHOD: Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior.

RESULTS: Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means.

CONCLUSIONS: Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions. PRACTICAL APPLICATIONS: The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety.

Language: en

Keywords

Humans; Adult; Female; Logistic Models; Male; Middle Aged; Adolescent; Young Adult; Risk-Taking; *Accidents, Traffic/statistics & numerical data/prevention & control; *Automobile Driving/statistics & numerical data; Influential factors; Quasi-induced exposure; Random parameter logit model with heterogeneity in means; Relative crash involvement ratio; Speeding driving