公交车群聚干预策略的实证分析与仿真
Analysis and simulation of intervention strategies against bus bunching by means of an empirical agent-based model
摘要：
在本文中，我们提出了一个基于经验的蒙特卡罗总线网络（EMB）模型作为一个测试床，模拟干预策略，以克服总线聚束的效率低下。EMB模型是一个基于主体的模型，它利用从全球定位系统（GPS）获得的巴士的位置和时间数据来构成：（1）一组巴士的经验速度分布，以及（2）一组乘客到达巴士站的间隔时间的指数分布。然后，对这两个导出的概率分布进行蒙特卡罗抽样，以产生公共汽车的运动和乘客的到达的随机动态。我们的EMB模型是通用的，可以应用到任何现实世界的总线网络系统。特别是，我们已经验证了该模型对南洋理工大学的穿梭巴士系统，证明其准确性，在捕捉到的群聚动态的穿梭巴士。此外，我们已经分析了三种干预策略的有效性：持有，不登机，和集中脉冲，对公共汽车群聚，将这些策略的规则集到模型中。在公交车具有相同速度的情况下，我们发现所有三种策略都改善了通勤者的等待时间和出行时间。然而，当总线具有不同的速度时，只有集中脉冲方案始终优于总线周期性地聚在一起的控制方案。
英文摘要：
In this paper, we propose an Empirically-based Monte Carlo Bus-network (EMB) model as a test bed to simulate intervention strategies to overcome the inefficiencies of bus bunching. The EMB model is an agent-based model which utilizes the positional and temporal data of the buses obtained from the Global Positioning System (GPS) to constitute: (1) a set of empirical velocity distributions of the buses, and (2) a set of exponential distributions of inter-arrival time of passengers at the bus stops. Monte Carlo sampling is then performed on these two derived probability distributions to yield the stochastic dynamics of both the buses' motion and passengers' arrival. Our EMB model is generic and can be applied to any real-world bus network system. In particular, we have validated the model against the Nanyang Technological University's Shuttle Bus System by demonstrating its accuracy in capturing the bunching dynamics of the shuttle buses. Furthermore, we have analyzed the efficacy of three intervention strategies: holding, no-boarding, and centralized-pulsing, against bus bunching by incorporating the rule-set of these strategies into the model. Under the scenario where the buses have the same velocity, we found that all three strategies improve both the waiting and travelling time of the commuters. However, when the buses have different velocities, only the centralized-pulsing scheme consistently outperform the control scenario where the buses periodically bunch together.
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。
