Intelligent Transportation Planning

如今，道路运输部门是温室气体排放统计的一个主要因素。这一贡献主要是由于使用传统化石燃料的车辆所占比例很大，以及道路上的车辆数量不断增加。然而，运输部门正在经历一场真正的技术革命，被称为运输4.0(TR4)，其主要内容是：1)向运输系统电气化过渡，2)采用自动驾驶和自动驾驶车辆(AVs)，3)信息技术的进步，4)运输数据的广泛收集和存储，以及5)对计算资源的即时和分布式访问。虽然TR4中隐含的技术进步为更可持续和更高效的运输系统创造了条件，但它们的管理变得更加具有挑战性。在考察科学文献时，人们发现，无论是在运输系统方面，还是在货运分配方面，都相当缺乏应对TR4提出的挑战的方法。因此，这项建议的主要目标是开发决策支持工具，使运输业能够充分从TR4提供的机会中获利。所提出的方法植根于运筹学和人工智能领域。这项研究计划围绕两个主题组织。第一个主题考虑了运输系统，并侧重于将按需服务与传统运输相结合所产生的规划问题。这是通过采用名为半柔性交通系统(STS)的建模框架来实现的。STSS的目标是在定期安排的运输框架内提供对需求反应的服务。STSS结合了传统系统和按需系统的特点，需要极其复杂的计划活动。更具体地说，该提案旨在开发将一支AVS机队纳入STSS运营规划的方法，并建立一个仿真框架来分析STSS与传统公交系统集成的性能及其对城市规模的机动性的影响。第二个主题考虑了货运，并侧重于TR4背景下出现的随机和动态路线问题。更具体地说，第二个主题旨在开发决策支持工具，用于在随机和动态交通条件下对车辆进行实时路线选择，以及在运营配备电池的电动汽车车队时的路线选择和充电操作的动态策略。通过开发支持TR4革命的工具，这项研究计划的预期影响是提供更可持续和更公平的交通服务，无论是在公共交通方面，还是在货运分配方面。

The road-transportation sector represents nowadays a major contributing factor to the count of Green-House Gas emissions. This contribution is mostly due to the large ratio of vehicles powered by traditional fossil fuels, and by the constant rise of the number of vehicles on the roads. The transportation sector has, however, the perspective of undergoing an authentic technological revolution, labelled as Transportation 4.0 (Tr4), whose main elements are: 1) the transition towards the electrification of the transportation system, 2) the adoption of self-driving and Autonomous Vehicles (AVs), 3) the advances in Information Technology, 4) the widespread collection and storage of transportation data, and 5) the instantaneous and distributed access to computing resources. While the technological advances implicit in the Tr4 set up the conditions for more sustainable and efficient transportation systems, their management becomes dramatically more challenging. When surveying the scientific literature, one finds that there is a considerable lack of methodologies to address the challenges presented by the Tr4, both in the context of transit systems, as well as in freight distribution. Therefore, the main objective of this proposal is to develop decision-support tools to enable the transportation industry to fully profit from the opportunities given by the Tr4. The proposed methods are rooted in the fields of operations research and artificial intelligence. This research program is organized around two themes. The first theme considers transit systems and focuses on planning problems arising in the integration of on-demand services with traditional transit. This is done by adopting a modeling framework named Semi-flexible Transit System (STS). STSs aim to provide demand-responsive service within the framework of regularly scheduled transit. Combining characteristics of traditional and on-demand systems, STSs require an extremely complex planning activity. More specifically, this proposal aims to develop the methodology to incorporate a fleet of AVs in the operations planning of STSs, and build a simulation framework to analyze the performance of a system integrating STSs with traditional transit, and its impact on the mobility at a city-scale. The second theme considers freight transportation and focuses on stochastic and dynamic routing problems arising in the context of the Tr4. More specifically, this second theme aims to develop decision-support tools for real-time routing of vehicles under stochastic and dynamic traffic conditions, as well as dynamic policies for routing and charging operations when operating a fleet of battery-equipped electric vehicles. By developing tools to enable the Tr4 revolution, the anticipated impact of this research program is to provide more sustainable and more equitable transportation services, both in terms of public transit, as well as freight distribution.