EAGER: Towards a Paradigm Shift in Data Acquisition for Traffic Control

收集交通数据的过程一直被认为是城市的重要组成部分之一，因为交通数据不仅用于旅行者信息系统，而且还作为支持城市街道交通控制的输入。在可预见的未来，我们设想在一个智能互联的城市中，目前基于道路的交通数据收集基础设施，如环路、微波传感器或激光探测器，将被基于车辆的数据所取代。这种模式的转变将大大节省成本，并产生一种比现有交通数据更强大的新形式。它将使我们的交通管理系统更高效，我们的城市更宜居。这个探索性研究早期概念资助（EAGER）项目的目的是加快这一进程，从而实现这种范式转变。该项目设想了一个系统，在这个系统中，司机通过向系统提供他们与时间相关的速度和定位信息来与交通控制系统互动，作为“投票”，以换取可能的优先服务。通过“投票”让系统用户参与系统控制将引起对尖端系统工程技术的兴趣，特别是对K-12学生。该研究将为理解智能互联社区交通管理系统的机遇提供重大进展，其中交通控制与个体驾驶员之间的互动是可行的。这种交互功能目前只适用于特殊级别的车辆，它将从根本上改变算法开发的传统理论。这可能会影响控制驾驶员路线选择行为和网络层面最优系统控制的用户均衡理论和系统最优理论。这个项目探索了一种新的算法，它直接利用空间数据，而不是目前使用的现场数据，来支持交通控制算法。新算法与现有算法的不同之处在于，关于交通状态的“定性信息”将被用作交通控制的输入。“非常长的队列”，而不是实际的队列长度)。这些信息可以很容易地从个别车辆的速度和定位数据中推断出来。个别车辆与系统控制之间的相互作用将使系统用户有机会告知系统他们的旅行价值。这种能力将需要重新思考用户平衡和其他旅行行为模式。

The process of collecting traffic data has long been considered as one of the important components of a city, because traffic data are used not only for traveler information systems but also as an input to support traffic control on urban streets. In the foreseeable future, we envision that in a smart and connected city the current roadway-based infrastructure for traffic data collection, such as loops, microwave sensors, or laser detectors, will be replaced by vehicle-based data. Such a paradigm shift would result in significant cost savings as well as the generation of a new form of traffic data that is more powerful than the existing one. It will make our traffic management systems more efficient and our cities more livable. The aim of this EArly-concept Grant for Exploratory Research (EAGER) project is to expedite the process that would lead to such a paradigm shift. The project envisions a system in which drivers interact with traffic control by providing their time-dependent speed and positioning information to the system as "votes" in exchange for possible priority service. Involving system users in system control through "voting" will engender interest in cutting-edge systems engineering techniques, especially for K-12 students. The research will provide significant advances in understanding opportunities for traffic management systems in smart and connected communities, where interaction between traffic control and individual drivers is practicable. Such interaction, a feature currently available only to special-class vehicles, would fundamentally change conventional theory for algorithm development. It could impact user-equilibrium and system optimum theories that govern route choice behavior of drivers and optimal system control at the network level. This project explores a new class of algorithms that directly utilize spatial data, as opposed to spot data used currently, to support traffic control algorithms. The new algorithms will differ from existing ones in that "qualitative information" about traffic states will be employed as an input to traffic control (e.g. "a very long queue" instead of the actual queue length). Such information can be readily inferred from speed and positioning data of individual vehicles. The interaction between individual vehicles and system control will provide system users with opportunities to inform the system of the value of their trips. Such capabilities will engender a need to rethink user equilibrium and other models of travel behavior.