Crowd simulation has been widely used as a tool to demonstrate the behavior of passengers on public transport. A simulation model allows researchers to evaluate the platform or interior designs without involving real-world experimentation. In this paper, we propose a passenger model to measure the effect of different public transport vehicle layouts on the required time for boarding and alighting. We first model a low level collision avoidance behavior based on an extended social force model aiming at simulating human interactions in confined spaces. The model introduces a mechanism to emulate rotation behavior while avoiding complex geometric computations and is calibrated to experimental data. The model also allows agents to perform collision prediction in low density environments. Strategical behavior of passengers is modeled according to the recognition-primed decision paradigm and combined with the collision avoidance model. We validate our model against real-world experiments from the literature, demonstrating deviations of less than 6%. In a case study, we evaluate the boarding and alighting times required by three autonomous vehicle interior layouts proposed by industrial designers in both low-density and high-density scenarios.

人群模拟已被广泛用作演示乘客在公共交通上的行为的工具。仿真模型使研究人员无需进行实际实验即可评估平台或室内设计。在本文中，我们提出了一种乘员模型，以衡量不同公共交通工具的布局对所需的上下车时间的影响。我们首先基于扩展的社会力量模型对低水平的避撞行为进行建模，该模型旨在模拟人类在密闭空间中的互动。该模型引入了一种模拟旋转行为的机制，同时避免了复杂的几何计算，并且已根据实验数据进行了校准。该模型还允许代理在低密度环境中执行碰撞预测。根据识别主导的决策范例对乘客的战略行为进行建模，并与避免碰撞模型相结合。我们根据文献中的实际实验验证了我们的模型，表明偏差小于6％。在一个案例研究中，我们评估了工业设计师在低密度和高密度场景中提出的三种自动驾驶汽车内部布局所需的上下车时间。