Optimization of disposition in case of disruptions from a passenger's view

该项目致力于研究公共交通延误和干扰对乘客的影响，并提出如何在运营处置中做出反应的建议。虽然我们已经建立了等待决策的面向决策者的处置基础，但我们项目的下一阶段将致力于开发方法和算法，以支持更复杂的处置用例（例如，取消，部分取消，短期周转，绕道，轨道变化）。为了在实践中应用，这些算法的实时运行至关重要。由于公共交通需求的不断增长和高峰时段的高占用率，可用车辆容量起着重要作用。在我们的项目中，我们将开发新的方法，考虑车辆容量的处置。其目的是引导乘客，以避免过度拥挤的车辆，并尽可能平衡车辆占用。 制定能够科普不可靠信息的方法，仍然是在发生中断时处置的一个主要挑战。在我们以前的工作中，我们已经表明，优化建议的处置决策强烈依赖于假设的客流量。因此，我们的目标是在额外数据来源的帮助下更准确地估计当前的客流量。异构数据源的融合及其动态更新带来了数据集成问题。 将扩大和完善稳健措施，以便量化中断和处置对客流的影响。特别是，我们将研究的鲁棒性的时间表已优化的空间和时间的动态需求。

This project is devoted to the impact of delays and disturbances on passengers in public transport and it develops recommendations how to react in operational disposition. While we have built the basis for a passenger-oriented disposition of waiting decisions, the next phase of our project addresses the development of methods and algorithms which can support more complex use cases of disposition (for example, cancellations, partial cancellations, short turnarounds, detours, track changes). To be applicable in practice, it is crucial that these algorithms run in real-time.Due to continuously increasing demand in public transport and high occupancy during rush hours, available vehicle capacities play an important role. In our project, we will develop new methods which consider vehicle capacities within disposition. The goal is to guide passengers so that overcrowded vehicles can be avoided and vehicle occupancy is as balanced as possible. The development of methods which are capable to cope with unreliable information remains a central challenge for disposition in case of disruptions. In our previous work we have shown that optimized recommendations for disposition decisions strongly depend on the supposed passenger flow. Therefore, we aim at more precise estimations of current passenger flows with the help of additional data sources. The fusion of heterogeneous data sources and their dynamic update leads to problems of data integration which have to be solved. Robustness measures which allow for a quantification of the impact of disruptions and disposition on passenger flows will be expanded and refined. In particular, we will study the robustness of timetables which have been optimized with respect to dynamic demand in space and time.