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

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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.