Theofilatos Athanasios, Yannis George
a National Technical University of Athens, School of Civil Engineering , Department of Transportation Planning and Engineering , Zografou-Athens , Greece.
Traffic Inj Prev. 2017 Apr 3;18(3):293-298. doi: 10.1080/15389588.2016.1198871. Epub 2016 Jun 21.
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.
了解影响事故风险的各种因素是决策者和研究人员特别关注的问题。在分析事故风险时，纳入实时交通和天气数据是一种富有成效的方法。然而，绝大多数相关研究并未特别关注电动两轮车（PTW）等弱势道路使用者。此外，使用城市道路和干线道路数据的研究很少。本研究旨在通过考虑希腊雅典市两条主要城市干线道路的实时交通和天气数据来增加当前的知识，以估计交通、天气和其他特征对PTW事故参与的影响。
由于候选变量数量众多，应用随机森林模型来揭示最重要的变量。然后，将潜在的重要变量用作贝叶斯逻辑回归模型的输入，以揭示它们对PTW事故参与的影响程度。
分析结果表明，PTW更有可能卷入多车事故而非单车事故。还表明交通流量增加和速度变化对PTW事故参与有显著影响。另一方面，发现天气特征没有影响。
本研究的结果有助于理解PTW的事故机制，并降低城市干线道路上PTW的事故风险。
