Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials. (June 2017)

• Record Type: Journal Article
• Authors: Theofilatos, Athanasios
• Is Part Of: Journal of safety research. Volume 61(2017:May)
• Page Start: 9
• Page End: 21
• Publication Date: 2017-06
• Subjects: Accident likelihood -- Accident severity -- Real-time data -- Urban arterials; Industrial safety -- Periodicals; Accidents -- Prevention -- Periodicals; Safety -- Periodicals; Accidents, Occupational -- Periodicals; Sécurité du travail -- Périodiques; Accidents -- Prévention -- Périodiques; Accidents -- Prevention; Industrial safety; Periodicals; 363.1
• DOI: 10.1016/j.jsr.2017.02.003
• Languages: English
• ISSNs: 0022-4375

Abstract

Abstract: Introduction: The effective treatment of road accidents and thus the enhancement of road safety is a major concern to societies due to the losses in human lives and the economic and social costs. The investigation of road accident likelihood and severity by utilizing real-time traffic and weather data has recently received significant attention by researchers. However, collected data mainly stem from freeways and expressways. Consequently, the aim of the present paper is to add to the current knowledge by investigating accident likelihood and severity by exploiting real-time traffic and weather data collected from urban arterials in Athens, Greece. Method: Random Forests (RF) are firstly applied for preliminary analysis purposes. More specifically, it is aimed to rank candidate variables according to their relevant importance and provide a first insight on the potential significant variables. Then, Bayesian logistic regression as well finite mixture and mixed effects logit models are applied to further explore factors associated with accident likelihood and severity respectively. Results: Regarding accident likelihood, the Bayesian logistic regression showed that variations in traffic significantly influence accident occurrence. On the other hand, accident severity analysis revealed a generally mixed influence of traffic variations on accident severity, although international literature states that traffic variations increase severity. Lastly, weather parameters did not find to have a direct influence on accident likelihood or severity. Conclusions: The study added to the current knowledge by incorporating real-time traffic and weather data from urban arterials to investigate accident occurrence and accident severity mechanisms. Practical application: The identification of risk factors can lead to the development of effective traffic management strategies to reduce accident occurrence and severity of injuries in urban arterials. Highlights: The study investigates accident likelihood and severity on urban arterials. Real-time traffic and weather data are utilized. Bayesian and finite mixture logit models were deployed. Traffic variations had a significant effect on accident occurrence but mixed effects on accident severity. No significance effect of weather parameters was found to exist.