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。加拿大安大略的大多伦多和汉密尔顿地区（GTHA）是一个生动的例子，所有模式，特别是道路都普遍拥堵。拥堵问题的主要根源包括：（1）缺乏科普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）在分配需求到供应方面缺乏效率，可能是在真实的时间，（4）无法控制的城市扩张，以及（5）缺乏综合的需求和供应管理分析工具。 建议的研究重点是动态拥挤定价作为一个有针对性的方法/措施，以管理需求产生和供应分配，并诱导对城市可持续发展的出行行为的变化。世界各地的众多案例研究和试点部署已经调查并证明了拥挤收费计划在减少拥挤城市地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干扰，利益相关者继续在是否收费的困境中挣扎，特别是在加拿大。这些挑战涉及的困难，制定和同时解决多方面的性质，拥挤收费问题纳入一个分析框架，可以提供定量的答案，拟议的定价计划和政策。由于缺乏准确和分析性的答案，政策制定者准备不足，公众感到恐惧，最终导致政策犹豫不决，公众强烈反对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（位置和时间特定）拥挤定价框架，解决关键问题，同时考虑三个重要的和相互关联的角度：经济，交通工程和出行行为。关键问题包括：（1）何时何地实施定价，（2）最优的时空定价结构，（2）对交通拥堵和道路通行能力的影响（供给方），（3）对社会福利的影响，（4）对出行者行为和选择维度（包括方式选择、出发时间选择和路线选择）的影响（需求方）。 建议的框架/系统是基于：1）控制和优化方法的需求和供应管理，2）用户离散的选择，以响应定价政策，和3）实时测量，以产生实时动态收费，可以防止潜在的交通故障。拟议的系统是动态的、随机的，适用于区域范围或特定设施，对距离、位置和一天中的时间敏感，在最小出行成本方面最大化网络性能，通过避免垄断定价和解决外部成本来最大化社会福利拥堵，并明确考虑需求弹性和用户在模式选择、出发时间选择、路线选择。 此外，随着普适传感器的市场渗透率的增加，预计在不久的将来，污染可以从车载设备直接测量并经由互联网以真实的时间进行通信。拟议的平台将尝试将拥堵定价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.