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Capacity-Boosting Congestion-Reduction Real-Time Dynamic Congestion Pricing

Problem Definition
  •  Road pricing has the potential to influence travel choices in a manner that reduces congestion while raising funds for improving sustainable transportation infrastructure.
  • It can also be used as a supply control policy that can eliminate hyper-congestion that arises when traffic densities increase the critical density (at capacity flow).
  • Controlling hyper-congestion prevents capacity loss due to traffic breakdown at higher densities, which, increases road capacity during peak periods.
  • Reducing congestion while increasing capacity is a win-win approach to congestion pricing, in addition to the benefits of raised funds for sustainable transportation infrastructure.

Literature and Background

 Numerous studies have investigated congestion pricing models and the best pricing structure to be used in congested urban areas. The scope of these studies ranges from applying a simple and flat pricing structure on a small (and sometimes hypothetical) network to a rigorous network-wide optimized pricing scheme.

Although these studies contributed considerably to the state-of-the-art and state-of-the-practice in congestion pricing, they still suffer from a number of limitations that limit their use in large and complex urban areas. For example:

  • Case studies on large urban networks were found scarce.
  • Although a few studies focused on non-flat (variable) pricing of an entire network; they mostly relied on hypothetical scenarios that lack theoretical justification.
  • In addition, travelers' individual responses to pricing (e.g. mode choice, route choice, and departure-time choice) were usually disregarded in many studies.
  • Also, most non-flat tolls lacked handling unpredicted disturbances through congestions as they were varied according to a fixed schedule rather than real-time traffic measurements.

Approach and Impact

This project aims at developing a practical congestion pricing strategy that is based on:

  1. Users discrete choices in response to what-if policies.
  2. Control and optimization approaches for demand and supply management.

This strategy should therefore consider:

  • Robust optimization methods to determine the optimum link toll schedules that produce the minimum total travel delay.
  • Users' stochastic responses to pricing by incorporating a discrete-choice framework to the dynamic congestion pricing model.
  • Real-time measurements to produce real-time dynamic tolls that can prevent potential traffic breakdowns (due to unpredicted disturbances).


Introduction to Congestion Pricing

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