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，这些数据捕捉了人们的出行偏好和详细的货物流动。新兴的运输服务（例如，共享出行、面向人员和货运的网约车）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），其中私人和公共交通模式之间以及人与货物之间的历史分离变得模糊。虽然这种综合运输系统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，制定符合可持续发展的政策和法规。在这种情况下，最基本的是需求预测和考虑需求响应的TS供应优化。该RP的预期结果是可以集成到中央当局的数据驱动决策支持工具中的方法，以分析当今工具无法解决的问题。更准确地说：(1)为政策制定提供定量支持；(2)设计和规划可持续的综合技术服务：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。******在这种情况下，出现了几个重要的挑战：首先，有不同类型的用户，他们具有异构的、有时甚至是相互冲突的偏好，在共享系统容量时相互作用。第二，将不同的人货运输方式整合在一起，一次出行可以使用多种运输方式。虽然集成有可能减少低效率，但最终的系统规模很大。第三，随着时间的推移，系统状态是动态的。所提出的RP的独创性在于我们解决的问题的范围和复杂性（考虑随机需求响应的优化供应），以及我们提出的创新方法，通过结合计算机科学和应用数学的几个子领域（机器学习，数学规划，近似动态规划）的方法来解决这些问题。***********

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). ***********