A hierarchical framework for congestion pricing of transportation networks

To further advance road pricing to be a more efficient and pragmatic tool for congestion mitigation, this dissertation proposes a hierarchical congestion pricing framework for urban transportation networks. Within the framework, toll determination is decomposed into two levels: network and facilities. Empirical studies have discovered that travelers have a strong preference for simple system-wide pricing structures. For example, dynamic network pricing models are not only difficult to implement, more importantly, their pricing signals are also too complicated for travelers to understand and consequently change their travel behaviors. On the other hand, time-varying tolls at particular facilities, such as managed lanes, are acceptable and effective. Therefore, tolls at these two levels should follow different strategies due to their distinctive purposes and travelers' different response abilities.;At the network level, we propose a robust static or time-of-day pricing policy to avoid complex toll structures while ensuring the network to perform reasonably well against a variety of uncertainties. Sources of uncertainty in transportation networks consist of not only randomness in demand and supply, but also travelers' stochastic and irrational behaviors. This dissertation investigates one of the uncertainties resulting from boundedly rational route-choice behaviors. Users with bounded rationality seek for acceptable paths rather than a necessarily minimum one. Boundedly rational user equilibrium (BRUE) flow distribution is generally nonunique and can be characterized as a non-convex and non-empty path flow set. A more restrictive link-based representation is also presented. A robust pricing scheme is determined by solving a nonlinear mathematical program with complementarity constraints to minimize the system travel time of the worst-case tolled BRUE flow distribution.;At some critical facilities, the toll scheme determined at the network level may be further adjusted in response to real-time traffic conditions. This dissertation focuses on developing pricing strategies for managed toll lanes. Adaptive tolls may be adopted in order to provide a superior free-flow travel service to the users of the toll lanes while maximizing the freeway's throughput. Two sensible and practically implementable approaches, one feedback and one self-learning, are proposed. The self-learning approach monitors conditions of the facility through both direct observation and real-time estimation, and learns recursively motorists' willingness to pay and short-term future demand by mining the traffic data from sensors. In determination of the tolls, a detailed modeling of drivers' lane-choice behavior and traffic dynamics is adopted to explicitly consider their impacts on the performance of the facility.;In summary, based on practical considerations of pricing, robust time-of-day tolls are proposed for the entire network while adaptive tolls are advocated for special facilities. This composes a hierarchical congestion pricing framework for a transportation system. Feasibility of this framework is shown through discussions on its general inputs and concerns. Our investigation on the network-level robust pricing with boundedly rational user behavior and the facility-level traffic-responsive pricing of managed lanes demonstrates that the proposed hierarchical framework may be practical and promising for congestion mitigation. (Full text of this dissertation may be available via the University of Florida Libraries web site. Please check http://www.uflib.ufl.edu/etd.html)...