为骑自行车者和其他非车道道路使用者设计的运动和互动模型

IF 1.9
Heather Kaths
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引用次数: 0

摘要

骑自行车的人和其他不遵守车道规则的道路使用者在微观交通模拟中构成了挑战。在大多数软件中,模型通过引入子车道(SUMO)或引入连续的横向轴(PTV Vissim)来增加道路使用者的横向灵活性。这些解决方案可以模拟某些行为,例如在同一行驶车道内超车。然而,这些灵活的道路使用者所表现出的其他行为,包括在自行车基础设施、道路和人行道之间切换,逆给定的行驶方向骑行,以及在十字路口选择意想不到的路径,仍然难以模拟。本文提出了一种针对骑自行车者、微移动模式使用者和其他非车道道路使用者的建模方法。这种方法使用指导方针的概念,或期望线,表示非基于车道的道路使用者的预期路径。指引的形式与道路(分)车道的中心线相同。指导方针不是精确地遵循这些线,而是用于确定道路使用者在下一个时间步骤中所需的方向,这被用作自适应社会力量类型模型的输入。基于NOMAD行人动力学模型,建立了行人运动与交互模型。单个加速度矢量被分为速度分量和方向分量,并使用来自德国慕尼黑四个信号交叉口的骑行者轨迹数据进行校准和验证。使用最大似然估计(MLE)来估计模型参数,并使用k-fold交叉验证来评估建模方法。对研究结果进行了讨论,并对今后的研究进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A movement and interaction model for cyclists and other non-lane-based road users
Cyclists and other types of road users who do not adhere to lane discipline pose a challenge in microscopic traffic simulation. In most software, the models are adapted to increase the lateral flexibility of road users, either through introducing sub-lanes ( SUMO ) or introducing a continuous lateral axis ( PTV Vissim ). These solutions enable the simulation of some behaviors, such as passing within the same driving lane. However, other behaviors exhibited by these flexible road users, including switching between cycling infrastructure, the roadways, and the sidewalk, riding against the given direction of travel, and selecting unexpected pathways to cross at intersections, remain difficult to simulate. This paper presents a modeling approach for cyclists, users of micro-mobility modes, and other non-lane-based road users. This method uses the concept of guidelines, or desire lines, that represent the intended path of non-lane-based road users. Guidelines are the same in form as the center line of road (sub-)lanes. Instead of following these lines precisely, the guideline is used to determine the desired direction for the road user in the next time step, which is used as input into an adapted social force type model. The movement and interaction model is formulated based on the NOMAD model for pedestrian dynamics. The single acceleration vector is divided into a speed component and a direction component that are calibrated and validated using trajectory data from cyclists at four signalized intersections in Munich, Germany. Maximum Likelihood Estimation (MLE) is used to estimate the model parameters and k-fold cross-validation is used to evaluate the modeling approach. The results are discussed and an outlook for future research is presented.
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