Risk Oriented Dispatching of Railway Operation under the Consideration of Random Disturbances in Dynamic Circumstances

随着交通需求的增加和基础设施扩建的有限，铁路网络经常接近满负荷运营，特别是在使用频繁的地区。结果，基本时刻表很容易受到操作干扰的影响，从而延迟的传播和累积显著降低了对客户的服务水平。为了解决这个问题，已经进行了广泛的研究，重点是预定义的鲁棒时间表和真实的时间调度算法的开发。然而，人们普遍认识到，过于严格的时间表可能会降低铁路网络的运营能力，并且在拥挤的地区难以实施恢复时间和缓冲时间的增加。此外，大多数传统的调度算法忽略了进一步的潜在的随机干扰，在调度过程中，这会产生不可执行的调度解决方案，并作为后果，准时性差和重复的调度行动。为此，本项目的目的是开发一种新的算法，为现实世界的调度过程中考虑风险导向的随机干扰的动态环境。在该项目的过程中，首先将产生一个操作风险图：通过模拟大量的时间表与随机干扰的Monte Carlo计划，并计算相应的总加权等待时间，不同的风险水平将分配给每个块段在所研究的铁路网络。在滚动时域框架内，通过在每个块中包含风险导向的随机扰动来检测冲突，并使用Tabu搜索算法计算近优调度解。最后，验证实验将在一个演示使用现有的软件PULEIV进行评估的有效性和优势，所提出的调度算法的三个相关指标：相对重排序，总加权等待时间和容量。所提出的算法预计将能够自动产生接近最优和强大的调度解决方案，具有足够的准时性和能力。

With the increasing traffic demand and limited infrastructure expansion, railway networks are often operated close to the full capacity, especially in heavily used areas. As a result, the basic timetable is quite susceptible to the operational disturbances, and thereby the propagation and accumulation of delays significantly degrade the service level for customers. To solve this problem, extensive researches have been conducted by focusing on the predefined robust timetables and the real time dispatching algorithm development. However, it has been widely recognized that excessive robust timetables may deteriorate the operating capacity of the railway network and the addition of recovery time and buffer time can be hardly implemented in the congested area. Moreover, most of the conventional dispatching algorithms ignore the further potential random disturbances during the dispatching process, which yield non-implementable dispatching solutions and, as consequences, inferior punctuality and repetitive dispatching actions. To this end, this project aims to develop a new algorithm for real-world dispatching process with the consideration of risk-oriented random disturbances in dynamic circumstances. In the procedure of this project, an operational risk map will be firstly produced: by simulating considerable amount of timetables with random disturbances in a Monte Carlo scheme and calculating the corresponding total weighted waiting time, different levels of risk will be assigned to each block section in the studied railway network. Within a rolling time horizon framework, conflicts are detected with the inclusion of risk-oriented random disturbances in each block, and the near optimal dispatching solutions are calculated by using Tabu search algorithm. Finally, validation experiments will be conducted in a demonstrator using the available software PULEIV to evaluate the effectiveness and advantage of the proposed dispatching algorithm by three related metrics: number of relative reordering, total weighted waiting time and capacity. The proposed algorithms are expected to be capable of automatically producing near-optimal and robust dispatching solutions with sufficient punctuality and capacity achieved.