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

在加拿大和世界各地的大城市和大都市，交通拥堵正达到危机水平。加拿大安大略省的大多伦多和汉密尔顿地区(GTHA)是所有交通方式普遍拥堵的生动例子，特别是道路。拥堵问题的主要根源包括：(1)缺乏应对人口增长的基础设施支出(缺乏充足的供应)，(2)不可持续的出行行为和选择以及严重依赖汽车(过度需求)，(3)缺乏将需求分配给供应的效率，可能是实时的(4)肆无忌惮的城市扩张，以及(5)缺乏综合需求和供应管理分析工具。**拟议的研究侧重于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发展的方向变化。世界各地的大量案例研究和试点部署已经调查和展示了拥挤收费计划在减少拥挤市区的车辆需求方面的潜力以及公众的反应。然而，由于技术、社会和政治挑战的重大交织，利益攸关方仍在为是否收费的两难境地而苦苦挣扎，特别是在加拿大。这些挑战涉及将拥堵收费问题的多方面性质同时纳入一个能够为拟议的定价方案和政策提供量化答案的分析框架中的困难。这种缺乏准确和分析的答案使政策制定者缺乏准备，公众感到恐惧，最终导致政策犹豫不决，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。**这项研究将开发一个统一的动态时空(位置和时间特定)拥堵定价框架，该框架解决了关键问题，同时考虑了三个相互关联的重要角度：经济、交通工程和出行行为。关键问题包括：(1)何时何地定价，(2)最优时空定价结构是什么，(2)对交通拥堵和道路通行能力(供给侧)有什么影响，(3)社会福利有什么影响，(4)对出行者的行为和选择维度(包括方式选择、出发时间选择和路线选择(需求侧))有什么影响。**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，以产生实时动态通行费，以防止潜在的交通故障。该系统是动态的、随机的，适用于区域或特定的设施，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外部拥堵成本最大化社会福利，并明确考虑需求弹性和用户在方式选择、发车时间选择和路线选择方面的行为反应。**此外，随着普及型传感器市场渗透率的提高，预计在不久的将来，可以直接从车载设备测量污染，并通过互联网实时通信。拟议的平台将尝试将拥堵定价与每辆汽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，并增强定价公平性。

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.