Theofilatos Athanasios
National Technical University of Athens, School of Civil Engineering, Dept. of Transportation Planning and Engineering, 5, Iroon Polytechneiou Str., Zografou Campus, Zografou-Athens GR-15773, Greece.
J Safety Res. 2017 Jun;61:9-21. doi: 10.1016/j.jsr.2017.02.003. Epub 2017 Mar 2.
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.
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.
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.
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.
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.
由于人员伤亡以及经济和社会成本，道路交通事故的有效处理以及道路安全的提升是社会主要关注的问题。利用实时交通和天气数据对道路事故可能性和严重程度进行调查最近受到了研究人员的广泛关注。然而，收集的数据主要来自高速公路和快速路。因此，本文的目的是通过利用从希腊雅典城市干道收集的实时交通和天气数据来调查事故可能性和严重程度，从而增加当前的知识。
首先应用随机森林（RF）进行初步分析。更具体地说，旨在根据候选变量的相关重要性对其进行排序，并对潜在的重要变量提供初步见解。然后，应用贝叶斯逻辑回归以及有限混合和混合效应logit模型分别进一步探索与事故可能性和严重程度相关的因素。
关于事故可能性，贝叶斯逻辑回归表明交通变化对事故发生有显著影响。另一方面，事故严重程度分析显示交通变化对事故严重程度的影响总体上较为复杂，尽管国际文献表明交通变化会增加严重程度。最后，未发现天气参数对事故可能性或严重程度有直接影响。
该研究通过纳入城市干道的实时交通和天气数据来调查事故发生情况和事故严重程度机制，增加了当前的知识。
识别风险因素可导致制定有效的交通管理策略，以减少城市干道事故的发生和伤害的严重程度。
Zotero 插件