Spatiotemporal Closed-Loop-Optimal Dynamic Road Pricing for Urban Congestion Control

在加拿大和世界范围内，大城市和大都市的交通拥堵已经达到了危机的程度。加拿大安大略省的大多伦多和汉密尔顿地区（GTHA）就是一个生动的例子，就各种交通方式，尤其是道路上的普遍拥堵而言。拥堵问题的关键根源包括：(1)缺乏应对人口增长的基础设施支出（缺乏足够的供应）；(2)不可持续的出行行为和选择以及对汽车的严重依赖（过度需求）；(3)需求分配效率低下，可能是实时的；(4)不受控制的城市蔓延；(5)缺乏综合需求和供应管理分析工具。**建议的研究重点是将动态拥堵定价作为一种有针对性的方法/措施来管理需求产生和供应分配，并诱导出行行为朝着城市可持续性的方向变化。世界各地的许多案例研究和试点部署已经调查并证明了拥堵收费计划在减少拥挤城市地区车辆需求方面的潜力，以及公众的反应。然而，利益相关者继续挣扎，特别是在加拿大，是否对他们的道路收费的困境；由于技术、社会和政治挑战交织在一起。这些挑战与将拥堵定价问题的多面性纳入一个分析框架的难度有关，该分析框架可以为拟议的定价方案和政策提供定量的答案。由于缺乏精确和分析性的答案，政策制定者缺乏准备，公众感到恐惧，最终导致政策优柔寡断，公众强烈反对可能是决定性和可持续的交通解决方案，即交通和政治僵局。**本研究将开发一个统一的动态时空（特定地点和时间）拥堵收费框架，在考虑经济、交通工程和出行行为这三个重要且相互关联的角度的同时，解决关键问题。关键问题包括：(1)何时何地实施定价，(2)最优时空定价结构是什么，(2)对交通拥堵和道路容量的影响（供给侧），(3)社会福利的影响，(4)对旅客行为和选择维度（包括模式选择、出发时间选择和路线选择）的影响（需求侧）。**建议的框架/系统基于：1)需求和供应管理的控制和优化方法，2)用户响应定价政策的离散选择，以及3)实时测量以产生实时动态收费，可以防止潜在的交通中断。该系统具有动态、随机、适用于大区域或特定设施，对距离、位置和时间敏感，以最小出行成本最大化网络性能，通过避免垄断定价和解决拥堵的外部成本实现社会福利最大化，并在模式选择、出发时间选择和路线选择方面明确考虑需求弹性和用户行为反应。**此外，随着无处不在的传感器市场渗透率的增加，预计在不久的将来，污染可以直接从车内设备测量，并通过互联网实时通信。拟议的平台将尝试将拥堵收费与每辆车造成的污染水平联系起来，以减少拥堵对环境的不利影响，并提高定价的公平性。

Traffic congestion is reaching a crisis level in larger cities and metropolises in Canada and worldwide. The Greater Toronto and Hamilton Area (GTHA) in Ontario, Canada, is a vivid example in terms of widespread congestion on all modes, particularly roads. Congestion problems have key roots including: (1) lack of infrastructure spending to cope of population growth (lack of adequate supply), (2) unsustainable travel behavior and choices and heavy dependence of cars (excessive demand), (3) lack of efficiency in allocating demand to supply, possibly in real time (4) untamed urban sprawl, and (5) lack of integrated demand and supply management analytical tools. **The proposed research focuses on dynamic congestion pricing as a pointed method/measure to manage demand generation and supply allocation and induce travel behavior changes towards urban sustainability. Numerous case studies and pilot deployments worldwide have investigated and demonstrated the potential of congestion pricing schemes in reducing the vehicular demand in congested urban areas, and the public's response. However, stakeholders continue to struggle, particularly in Canada, with the dilemma of whether to toll or not to toll their roads; due to significant intertwine of technical, social and political challenges. These challenges are related to the difficulty of formulating and concurrently addressing the multifaceted nature of the congestion pricing problem into one analytical framework that can provide quantitative answers to proposed pricing schemes and policies. Such lack of precise and analytical answers renders policy makers ill equipped and the public fearful, ultimately leading to policy indecision and fierce public opposition to what can be a decisive and sustainable transportation solution, i.e. transportation and political gridlock. **This research will develop a unified dynamic spatio-temporal (location and time specific) congestion pricing framework that tackles the key questions while considering three important and interrelated perspectives: economic, traffic engineering and travel behavior. The key questions include: (1) where and when to apply pricing, (2) what is the optimal spatio-temporal pricing structure, (2) what is impact on traffic congestion and road capacities (supply side), (3) what is the impact of social welfare, (4) what is the impact on travellers' behavior and choice dimensions including mode choice, departure time choice and route choice (demand side). **The proposed framework/system is based on: 1) control and optimization approaches for demand and supply management, 2) users discrete choices in response to pricing policies, and 3) real-time measurements to produce real-time dynamic tolls that can prevent potential traffic breakdowns. The proposed system is dynamic, stochastic, applicable to area wide or specific facilities, sensitive to distance, location and time of the day, maximizes network performance in terms of minimum travel cost, maximizes social welfare by avoiding monopoly pricing and addressing the external cost of congestion, and explicitly accounts for demand elasticity and users' behavioral responses in terms of mode choice, departure time choice, and route choices. **Further, with the increase of market penetration of pervasive sensors, it is anticipated in the near future that pollution can be directly measured from in-vehicle devices and communicated in real time via the Internet. The proposed platform will attempt to relate congestion pricing to the pollution level each individual vehicle contributes, to reduce adverse environmental impacts of congestion and enhance pricing equity.