College of Transportation Engineering, Chang'an University, Xi'an 710064, PR China.
College of Transportation Engineering, Chang'an University, Xi'an 710064, PR China.
Accid Anal Prev. 2022 Oct;176:106810. doi: 10.1016/j.aap.2022.106810. Epub 2022 Aug 29.
Pedestrian vehicle conflicts at non-signalized crosswalks are a world-wide safety concern. Although the "pedestrian priority" policy is applied in some regions to improve pedestrian safety, its effect needs further investigation. This study proposes the Lane-based Distance-Velocity model (LDV) to investigate pedestrian-vehicle interaction at non-signalized crosswalks. Compared with the DV model, the LDV model considers the lateral distance between vehicles and pedestrians. Therefore, the LDV model extends the application of the DV model by allowing it to be applied not only on one-lane streets to multi-lane streets. The conflict severities of pedestrian-vehicle interaction in the LDV model are classified into four categories: safe-passage, mild-interaction, potential-conflict and potential-collision. Based on that, pedestrian crossing decisions are graded as safe-crossing, risky-crossing, and dangerous-crossing. The experimental data are collected at a non-signalized crosswalk through drone footage collected in Xi'an City (China) with a Machine Vision Intelligent Algorithm. The model is tested through a case study to evaluate pedestrian crossing safety when interacting with private cars and taxis. Results from the case study suggest that the proposed model works well in the pedestrian-vehicle interaction analysis. Firstly, 87.9% of drivers are willing to provide right-of-way to pedestrians when they have enough time to react and yield. Then, both the DV model and LDV model have reached consistent conclusions: the deliberate violation rate (DVR) of taxi drivers is 22.64%, which is double that of private car drivers. Last, taxis commit a higher percentage of pedestrians' dangerous or risky crossing situations than private cars. Relevant government departments can utilize the results of this study to manage urban traffic better and improve pedestrian safety.
行人和车辆在非信号交叉口的冲突是一个全球性的安全问题。尽管在一些地区采用了“行人优先”政策来提高行人安全性，但需要进一步研究其效果。本研究提出了基于车道的距离-速度模型（LDV）来研究非信号交叉口的行人和车辆相互作用。与 DV 模型相比，LDV 模型考虑了车辆和行人之间的横向距离。因此，LDV 模型通过允许其不仅应用于单车道街道，还应用于多车道街道，扩展了 DV 模型的应用。LDV 模型中行人-车辆相互作用的冲突严重程度分为四类：安全通过、轻度交互、潜在冲突和潜在碰撞。在此基础上，行人的过街决策被分为安全过街、危险过街和危险过街。实验数据是通过在中国西安市使用机器视觉智能算法从无人机拍摄的视频中收集的非信号交叉口收集的。该模型通过案例研究进行了测试，以评估与私家车和出租车相互作用时行人过街的安全性。案例研究的结果表明，所提出的模型在行人和车辆相互作用分析中表现良好。首先，当司机有足够的时间做出反应和避让时，87.9%的司机愿意给行人让路。然后，DV 模型和 LDV 模型都得出了一致的结论：出租车司机的故意违法率（DVR）为 22.64%，是私家车司机的两倍。最后，出租车造成的行人危险或危险过街情况比私家车更多。相关政府部门可以利用本研究的结果更好地管理城市交通，提高行人安全性。
