Abstract

There has been a significant increase in the number of motorcyclists in the past years. According to crash statistics, motorcyclist crashes involving roadside fixed objects and safety systems have a higher probability of resulting in a fatality and serious injury. Therefore, as the number of motorcyclists on the road increases, there is an urgent need to consider motorcyclist safety as an important factor when designing roadside safety systems. In this paper, we are developing a hot-spot detection methodology to identify the design characteristics of locations associated with severe motorcyclist crashes for improving roadside safety systems. For this purpose, we are using the multinomial logistic regression and data mining tools such as random forests and decision trees. The results of both data mining and regression analysis show that roadside safety devices and fixed objects have a significant impact on RwD motorcyclist crash severity. Additionally, roadway characteristics (horizontal and vertical curvature, lane width, urban-rural classification) and operational factors (traffic volume and posted speed limit) are found to be associated with the RwD motorcyclist crash severity. Hence, to identify the high-risk locations (i.e., locations with potential for improvement), these roadway elements need to be accounted for.

摘要

过去几年，骑摩托车的人数显着增加。根据碰撞统计，涉及路边固定物体和安全系统的摩托车碰撞事故导致死亡和重伤的概率较高。因此，随着道路上骑摩托车的人数不断增加，在设计路边安全系统时，迫切需要将骑摩托车的安全作为重要因素考虑。在本文中，我们正在开发一种热点检测方法，以识别与严重摩托车事故相关的位置的设计特征，以改善路边安全系统。为此，我们使用多项逻辑回归和数据挖掘工具，例如随机森林和决策树。数据挖掘和回归分析的结果表明，路边安全装置和固定物体对 RwD 摩托车手碰撞严重程度有显着影响。此外，发现道路特征（水平和垂直曲率、车道宽度、城乡分类）和运营因素（交通量和公布的限速）与 RwD 摩托车手碰撞严重程度有关。因此，为了识别高风险位置（即具有改进潜力的位置），需要考虑这些道路要素。城乡分类）和运营因素（交通量和公布的限速）被发现与 RwD 摩托车手碰撞严重程度有关。因此，为了识别高风险位置（即具有改进潜力的位置），需要考虑这些道路要素。城乡分类）和运营因素（交通量和公布的限速）被发现与 RwD 摩托车手碰撞严重程度有关。因此，为了识别高风险位置（即具有改进潜力的位置），需要考虑这些道路要素。