Investigation of powered 2-wheeler accident involvement in urban arterials by considering real-time traffic and weather data.
Understanding the various factors that affect accident risk is of particular concern to decision makers and researchers. The incorporation of real-time traffic and weather data constitutes a fruitful approach when analyzing accident risk. However, the vast majority of relevant research has no specific focus on vulnerable road users such as powered 2-wheelers (PTWs). Moreover, studies using data from urban roads and arterials are scarce. This study aims to add to the current knowledge by considering real-time traffic and weather data from 2 major urban arterials in the city of Athens, Greece, in order to estimate the effect of traffic, weather, and other characteristics on PTW accident involvement.



Because of the high number of candidate variables, a random forest model was applied to reveal the most important variables. Then, the potentially significant variables were used as input to a Bayesian logistic regression model in order to reveal the magnitude of their effect on PTW accident involvement.



The results of the analysis suggest that PTWs are more likely to be involved in multivehicle accidents than in single-vehicle accidents. It was also indicated that increased traffic flow and variations in speed have a significant influence on PTW accident involvement. On the other hand, weather characteristics were found to have no effect.



The findings of this study can contribute to the understanding of accident mechanisms of PTWs and reduce PTW accident risk in urban arterials.
a National Technical University of Athens, School of Civil Engineering , Department of Transportation Planning and Engineering , Zografou-Athens , Greece.
Athanasios
A
Theofilatos
a National Technical University of Athens, School of Civil Engineering , Department of Transportation Planning and Engineering , Zografou-Athens , Greece.
George
G
Yannis
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D001642:Bicycling / Q000293:injuries*; D002947:Cities; D006115:Greece; D006801:Humans; D016015:Logistic Models; D009049:Motorcycles; D018570:Risk Assessment; D014887:Weather; D014947:Wounds and Injuries / Q000453:epidemiology*
Powered 2-wheelers; accidents; real-time data; urban arterials