Modal shift in public transport as a consequence of a disruption on a line has in some cases unforeseen consequences such as an increase in congestion in the rest of the network. How information is provided to users and their behavior plays a central role in such configurations. We introduce here a simple and stylised agent-based model aimed at understanding the impact of behavioural parameters on modal shift. The model is applied on a case study based on a stated preference survey for a segment of Paris suburban train network. We systematically explore the parameter space and show non-trivial patterns of congestion for some values of discrete choice parameters linked to perceived wait time and congestion. We also apply a genetic optimisation algorithm to the model to search for optimal compromises between congestion in different modes.

中文翻译：

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基于代理的公共交通模式转换模型

在某些情况下，由于线路中断而导致的公共交通模式转变会产生不可预见的后果，例如网络其余部分的拥堵加剧。如何向用户提供信息及其行为在此类配置中起着核心作用。我们在此介绍一个简单且风格化的基于代理的模型，旨在了解行为参数对模态转换的影响。该模型应用于基于对巴黎郊区火车网络段的既定偏好调查的案例研究。我们系统地探索参数空间并显示与感知等待时间和拥塞相关的离散选择参数的某些值的拥塞的非平凡模式。我们还将遗传优化算法应用于模型以搜索不同模式下拥塞之间的最佳折衷。