CPS: Breakthrough: Improving Metropolitan-Scale Transportation Systems with Data-Driven Cyber-Control

传统上，城市交通服务的设计一直基于通过调查和普查收集的有限抽样数据，这些数据往往是过时和不完整的。由于缺乏来自多种交通方式的海量在线馈送，通过网络控制很难实现供需关系的实时均衡，最终表现为多个城市交通问题：(I)由于没有供应而导致最后一英里的交通漫长，(Ii)由于供应不足而延长等待时间，以及(Iii)由于供应过剩而导致的闲置里程过多。该项目通过关注大都市地区的两种交通系统来解决这些问题：(I)公共自行车租赁共享系统和(Ii)以车队为导向的共享乘车系统。公共自行车租赁共享系统用于允许通勤者在公交站附近租用自行车，以完成最后一英里的旅程。以车队为导向的拼车系统为乘客之间的拼车安排了一支参与的车队，其中的拼车降低了乘客个人支付的费用，增加了出租车司机的利润，并可以提高服务的可用性。交通共享系统的理论和实践通常集中在孤立的个人交通方式上。该项目将从大都市信息基础设施收集海量多模式在线馈送，以建模交通系统的动态行为，然后利用海量微观级别的出行信息应用细粒度实时控制，以应对动态大都市环境的快速变化。将制定建设多式联运、综合城市交通系统的总体原则和设计方法。这些研究发现将应用于商业应用。解决自行车站点的长期部署问题，特别是在低收入地区，为站点部署和具体部署计划的评估提供建议。该项目还解决了自行车的短期维护问题，以平衡共享自行车的使用，以防止租赁自行车的快速恶化，并提高自行车租赁服务的实时可用性。该项目还将研究以车队为导向的拼车系统，该系统根据实时供需比决定车费，以应对动态的大都市场景。该项目将支持两名博士生，他们将通过与市政府和小企业的密切互动接受创新和技术翻译培训。这种伙伴关系加速采用尖端技术，评估业务环境中的研究解决方案，并获得用户反馈以触发进一步的创新。该项目将提高共享经济下现有交通系统的效率，最终将显著改善美国大都市地区的日常生活质量。

Traditionally, the design of urban transit services has been based on limited sampling data collected through surveys and censuses, which are often dated and incomplete. Lacking massive online feeds from multiple transit modes makes it hard to achieve real-time equilibrium in demand and supply relationship through cyber-control, which eventually manifests into multiple urban transportation issues: (i) lengthy last-mile transit due to non-supply, (ii) prolonged waiting due to undersupply, and (iii) excessive idle mileage due to oversupply. This project addresses these issues by focusing on two types of transportation systems in metropolitan areas: (i) public bike rental sharing systems and (ii) fleet-oriented ride sharing systems. The public bike rental sharing systems are used to allow commuters to rent bikes near public transit stations for the last mile of their trips. The fleet-oriented ride sharing systems schedule a fleet of participating vehicles for ride sharing among passengers in which shared ridership reduces individual fare paid by passengers, increases the profit of taxi drivers, and can improve the availability of service.The theory and practice of transportation sharing systems have typically focused on isolated individual transportation modes. The project will collect massive multi-modal online feeds from metropolitan information infrastructure to model dynamic behaviors of transportation systems, and then utilize massive micro-level trip information to apply fine-grained real-time control to handle rapid changes in dynamic metropolitan environments. General principles and design methodologies will be designed to build multi-modal, integrated urban transportation systems. These research discoveries will be applied toward commercial applications. Long-term deployment problem of bike stations will be addressed, especially in the low-income districts, to provide suggestions on the station deployment and assessment for specific deployment plans. The project also solves the short-term bike maintenance issue to balance the usage of shared bikes to prevent quick deterioration of rental bikes, and improve availability of bike rental services in real time. This project will also study fleet-oriented ride sharing systems that decide fares based on real-time supply/demand ratio to handle dynamic metropolitan scenarios.This project will support two Ph.D. students who will receive innovation and technology translation training through close interactions with municipal governments and small-business companies. Such partnerships expedite the adoption of cutting-edge technology, evaluate research solutions in operational environments, and obtain user feedback to trigger further innovations. The project will improve the efficiency of existing transportation systems under sharing economy and ultimately the work would noticeably improve the quality of every-day life in metropolitan areas across the United States.