Department of Civil, Geological and Mining Engineering, École Polytechnique de Montréal, 2900 Boulevard Edouard-Montpetit, B-344, Montréal, Québec, H3T 1J4, Canada.
Department of Civil, Geological and Mining Engineering, École Polytechnique de Montréal, C.P. 6079, succ. Centre-Ville, Montréal, Québec, H3C 3A7, Canada.
Accid Anal Prev. 2019 Oct;131:239-247. doi: 10.1016/j.aap.2019.07.006. Epub 2019 Jul 18.
The cycling safety research literature has proposed methods to analyse safety and case studies to better understand the factors that lead to cyclist crashes. Surrogate measures of safety (SMoS) are being used as a proactive approach to identify severe interactions that do not result in an accident and interpreting them for a safety diagnosis. While most cyclist studies adopting SMoS have evaluated interactions by counting the total number of severe events per location, only a few have focused on the interactions between general directions of movement e.g. through cyclists and right turning vehicles. However, road users perform maneuvers that are more varied at a high spatiotemporal resolution such as a range of sharp to wide turning movements. These maneuvers (motion patterns) have not been considered in past studies as a basis for analysis to identify, among a range of possible motion patterns in each direction of travel, which ones are safer, and which are more likely to result in a crash. This paper presents a novel movement-based probabilistic SMoS approach to evaluate the safety of road users' trajectories based on clusters of trajectories representing the various movements. This approach is applied to cyclist-vehicle interactions at two locations of cycling network discontinuity and two control sites in Montréal. The Kruskal-Wallis and Kolmogorov-Smirnov tests are used to compare the time-to-collision (TTC) distribution between motion patterns in each site and between sites with and without a discontinuity. Results demonstrate the insight provided by the new approach and indicate that cyclist interactions are more severe and less safe at locations with a cycling network discontinuity and that cyclists following different movements have statistically different levels of safety.
自行车安全研究文献提出了分析安全性的方法和案例研究，以更好地理解导致自行车事故的因素。替代安全措施 (SMoS) 被用作一种主动方法来识别不会导致事故的严重交互，并对其进行安全诊断。虽然大多数采用 SMoS 的自行车研究都通过计算每个位置的严重事件总数来评估交互，但只有少数研究关注一般运动方向之间的交互，例如自行车和右转车辆之间的交互。然而，道路使用者以更高的时空分辨率执行更具变化性的操作，例如各种急转弯和宽转弯动作。这些操作（运动模式）在过去的研究中并未被视为分析的基础，以确定在每种行驶方向的一系列可能运动模式中，哪些更安全，哪些更有可能导致事故。本文提出了一种新的基于运动的概率 SMoS 方法，该方法基于代表各种运动的轨迹簇来评估道路使用者轨迹的安全性。该方法应用于自行车网络不连续的两个位置和蒙特利尔的两个控制位置的自行车-车辆交互。Kruskal-Wallis 和 Kolmogorov-Smirnov 检验用于比较每个位置和具有和不具有不连续性的位置之间的轨迹模式之间的碰撞时间 (TTC) 分布。结果证明了新方法提供的见解，并表明在自行车网络不连续的位置，自行车交互更加严重且安全性更低，并且遵循不同运动的自行车具有统计学上不同的安全水平。
