Data-driven Optimization of Transport Systems

信息和通信技术(ICT)的进步以及车辆技术的进步正在深刻地改变我们运输人员和货物的方式。信息和通信技术为新的运输服务创造了机会，它们产生了大量的分类数据，收集了人们的流动偏好和详细的货物流动情况。新兴的运输服务(例如，共享移动性、人员和货运电子召唤)影响着人们和行业的短期和长期行为。预测这些行为变化并设计交通系统以鼓励可持续发展是当今社会面临的主要挑战之一。这些新兴运输服务的使用导致了综合运输系统(TS)，其中私营和公共运输方式以及人和货运之间的历史区别变得模糊。虽然这种集成的TSS在提高效率和减少排放方面具有巨大的潜力，但整个系统的设计、规划和优化是复杂的。拟议的研究计划(RP)侧重于这一具有挑战性的主题。重要的是，中央当局(如公共机构或其他利益攸关方)能够规划基础设施，确定与可持续发展相一致的政策和法规。在这种情况下，基本的是需求预测和考虑需求响应的TS供应的优化。这一方案的预期成果是可纳入中央当局以数据为导向的决策支持工具的方法论，以分析用今天的工具不可能解决的问题。更准确地说，(1)为决策提供量化支持；(2)设计和规划可持续的综合技术：提供适当的激励措施，鼓励人们和其他行为者的可持续行为。在这方面，出现了几个重要的挑战：首先，不同类型的用户具有不同的、有时甚至是相互冲突的偏好，他们在分享系统能力时相互作用。其次，人和货物的不同运输方式被整合在一起，使得一次出行可以使用多种方式。虽然整合有可能减少效率低下的情况，但由此产生的系统规模很大。第三，随着系统状态随着时间的推移而演变，TS是动态的。建议的RP的独创性在于我们解决的问题的范围和复杂性(在考虑随机需求响应的情况下优化供应)，以及我们通过结合计算机科学和应用数学(机器学习、数学规划、近似动态规划)的几个子领域的方法来解决这些问题的创新方法。

Advances in Information and Communication Technologies (ICT) alongside advances in vehicle technologies are profoundly transforming the way we transport people and freight. ICT create opportunities for new transport services and they generate tremendous amount of disaggregate data capturing people's mobility preferences and detailed freight movements. Emerging transport services (e.g., shared mobility, e-hailing for people and freight) impact the short and long-term behavior of people and industry. Predicting these behavioral changes and designing transport systems to encourage sustainable development constitutes one of the major challenges of today's society. The use of these emerging transport services results in integrated transport systems (TS) where the historical separations between private and public transport modes as well between people and freight are blurred. While such integrated TSs have a huge potential to improve efficiency and reduce emissions, the design, planning and optimization of the overall system is complex. The proposed Research Program (RP) focuses on this challenging topic. It is important so that central authorities (e.g., public agencies or other stakeholders) can plan infrastructure, define policies and regulations that are aligned with sustainable development. Fundamental in this context is the prediction of demand and the optimization of TS supply taking demand response into account. The expected outcome of this RP is methodologies that can be integrated in data-driven decision-support tools of central authorities to analyze questions that are not possible with the tools of today. More precisely (i) provide quantitative support for policy-making (ii) design and plan sustainable integrated TS: providing the right incentives to encourage sustainable behavior of people and other actors. In this context, several important challenges arise: First, there are different types of users with heterogeneous, sometimes even conflicting, preferences that interact when sharing the capacity of the system. Second, different transport modes for both people and goods are integrated so multiple modes can be used within one trip. While integration has the potential to decrease inefficiencies, the resulting systems are large scale. Third, TSs are dynamic as the state of the system evolves over time. The originality of the proposed RP lies in the scope and complexity (optimizing supply taking a stochastic demand response into account) of the problems we tackle and the innovative methodologies we propose to address them by combining methods from several subfields of computer science and applied mathematics (machine learning, mathematical programming, approximate dynamic programming).