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首页> 外文期刊>Promet-traffic & transportation >ANALYSIS OF ROADWAY TRAFFIC ACCIDENTS BASED ON ROUGH SETS AND BAYESIAN NETWORKS
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ANALYSIS OF ROADWAY TRAFFIC ACCIDENTS BASED ON ROUGH SETS AND BAYESIAN NETWORKS

机译：基于粗糙集和贝叶斯网络的道路交通事故分析

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摘要

The paper integrates Rough Sets (RS) and Bayesian Networks (BN) for roadway traffic accident analysis. RS reduction of attributes is first employed to generate the key set of attributes affecting accident outcomes, which are then fed into a BN structure as nodes for BN construction and accident outcome classification. Such RS-based BN framework combines the advantages of RS in knowledge reduction capability and BN in describing interrelationships among different attributes. The framework is demonstrated using the 100-car naturalistic driving data from Virginia Tech Transportation Institute to predict accident type. Comparative evaluation with the baseline BNs shows the RS-based BNs generally have a higher prediction accuracy and lower network complexity while with comparable prediction coverage and receiver operating characteristic curve area, proving that the proposed RS-based BN overall outperforms the BNs with/without traditional feature selection approaches. The proposed RS-based BN indicates the most significant attributes that affect accident types include pre-crash manoeuvre, driver's attention from forward roadway to centre mirror, number of secondary tasks undertaken, traffic density, and relation to junction, most of which feature pre-crash driver states and driver behaviours that have not been extensively researched in literature, and could give further insight into the nature of traffic accidents.
机译：本文将粗糙集（RS）和贝叶斯网络（BN）集成在一起，用于道路交通事故分析。首先采用属性的RS约简来生成影响事故结果的关键属性集，然后将其输入到BN结构中，作为BN构建和事故结果分类的节点。这种基于RS的BN框架结合了RS在减少知识方面的优势和BN在描述不同属性之间的相互关系方面的优势。该框架使用来自弗吉尼亚理工交通学院的100辆汽车的自然驾驶数据进行了演示，以预测事故类型。与基准BN的比较评估表明，基于RS的BN通常具有较高的预测准确性和较低的网络复杂性，同时具有可比较的预测覆盖范围和接收器工作特性曲线面积，证明了所建议的基于RS的BN总体上优于或不具有传统的BN特征选择方法。拟议的基于RS的BN表明，影响事故类型的最重要属性包括：碰撞前的动作，驾驶员从前行车道到中心镜的注意力，承担的次要任务数量，交通密度以及与路口的关系，其中大多数特征是撞车驾驶员状态和驾驶员行为尚未在文献中进行广泛研究，可能会进一步了解交通事故的性质。

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