共享空间中人车冲突解决的离散选择模型
A discrete choice model for solving conflict situations between pedestrians and vehicles in shared space
摘要：
当街道按照共享空间原则设计时，鼓励道路使用者自发地相互交流以协商空间。这些互动机制并不遵循明确定义的交通规则，而是与安全，舒适和时间压力有关的心理和社会原则。然而，这些原则是难以捕捉和量化，从而使其难以模拟的道路使用者的行为。本文研究了行人和机动车之间的交通冲突情况，主要目的是建立一个离散选择模型，以确定适当的冲突解决策略。在德国汉堡的共享空间街道，高行人流量被用作模型制定和校准的案例研究。通过轨迹预测和比较的自动程序检测冲突情况。通过观察行为模式，识别行人和车辆的标准规避行为。一组潜在的参数，这可能会影响选择的回避行动，制定和测试的意义。这些包括几何方面，如冲突用户的距离和速度，以及冲突特定的方面，如碰撞时间。最终在实际情况下对多项logit模型进行了校准和验证。所开发的方法是现实的，并准备在共享空间或任何其他组织较少的交通环境中的运动模型的实施。
英文摘要：
When streets are designed according to the shared space principle, road user are encouraged to interact spontaneously with each other for negotiating the space. These interaction mechanisms do not follow clearly defined traffic rules but rather psychological and social principles related to aspects of safety, comfort and time pressure. However, these principles are hard to capture and to quantify, thus making it difficult to simulate the behavior of road users. This work investigates traffic conflict situations between pedestrians and motorized vehicles, with the main objective to formulate a discrete choice model for the identification of the proper conflict solving strategy. A shared space street in Hamburg, Germany, with high pedestrian volumes is used as a case study for model formulation and calibration. Conflict situations are detected by an automatic procedure of trajectory prediction and comparison. Standard evasive actions are identified, both for pedestrians and vehicles, by observing behavioral patterns. A set of potential parameters, which may affect the choice of the evasive action, is formulated and tested for significance. These include geometrical aspects, like distance and speed of the conflicting users, as well as conflict-specific ones, like time to collision. A multinomial logit model is finally calibrated and validated on real situations. The developed approach is realistic and ready for implementation in motion models for shared space or any other less organized traffic environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
