Data-driven Optimization of Transport Systems

数据驱动的运输系统优化

• 批准号: RGPIN-2019-04538
• 负责人: RobertFrejinger, Emma
• 金额: $ 2.26万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714717
• 关键词: Data; driven; Optimization; Transport; Systems

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).

信息和通信技术 (ICT) 的进步以及车辆技术的进步正在深刻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，并产生大量分类数据，捕捉人们的出行偏好和详细的货运活动。新兴的交通服务（例如共享出行、人员和货运电子召车）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形成了综合运输系统（TS），其中私人和公共运输模式之间以及人员和货运之间的历史界限变得模糊。虽然这种集成传输系统在提高效率和减少排放方面具有巨大潜力，但整个系统的设计、规划和优化却很复杂。拟议的研究计划（RP）重点关注这一具有挑战性的主题。重要的是，中央当局（例如公共机构或其他利益相关者）可以规划基础设施，制定与可持续发展相一致的政策和法规。在这种情况下，最根本的是需求预测和考虑需求响应的 TS 供应优化。该RP的预期成果是可以将方法集成到中央当局的数据驱动决策支持工具中，以分析当今工具无法解决的问题。更准确地说，（i）为政策制定提供定量支持（ii）设计和规划可持续的综合技术服务：提供正确的激励措施以鼓励人们和其他参与者的可持续行为。 在这种情况下，出现了几个重要的挑战：首先，不同类型的用户具有异构的、有时甚至是冲突的偏好，在共享系统容量时相互作用。二是整合不同人、货运输方式，实现一程多用。虽然集成有可能降低效率，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变化，TS 是动态的。拟议的 RP 的独创性在于我们解决的问题的范围和复杂性（考虑随机需求响应来优化供应）以及我们建议通过结合计算机科学和应用数学的几个子领域的方法来解决这些问题的创新方法（机器学习、数学规划、近似动态规划）。