Traffic management through compromise solutions: A mathematical programming approach with multiple-objective traffic assignment models

Applications of traffic assignment models to modern advanced traffic network management systems have become mainstream both in academic research and field implementation. Most traffic assignment applications are aimed at how to implement Wardrop's (1952) two principles, user-equilibrium (UE) and system-optimal (SO). The UE and SO criteria have been widely discussed among academics and applied in traffic assignment models since Wardrop proposed these concepts in 1952.; The complexity of the traffic network management problem induced by the tradeoff between the user-oriented demand and limited system capacity supply is considered in this thesis. The problem is termed multiple-objective traffic assignment (MOTA). Beyond the user cost-oriented tradeoff, the MOTA formulation in this thesis explicitly models the tradeoffs between demand and supply in the system.; Due to the lack of physical interpretation of UE, this thesis introduces an alternate objective formulation defined as user variational (UV), which not only can find UE but also provide an interpretation measure of user service when UE cannot be obtained. This thesis also presents another new objective called congestion-optimal (CO) that defines traffic network manager's goal, which is to minimize the maximum arc congestion within the network. Comparisons are made between the new perspectives and the classic system-optimal perspective.; This thesis presents a mathematical programming approach that integrates a nonlinear convex optimization model, a mixed integer optimization model with multicriteria decision-making theory to solving the traffic network management problem while considering explicitly the effect of arc capacity on the traffic flow assignment. A new multiple-objective optimization solution approach is developed to solving the network management problem by applying UV, UE, CO and SO objective formulations as traffic network's performance measures in the traffic assignment model, which is integrated with a p-median model that served as the best compromise solution finding mechanism. This approach is implemented to networks with different complexities. Tradeoffs among objectives are discussed. The results show that in high demand situation, the compromise solution can not only reduce the system congestion significantly but also limit the impact to users. This MOTA approach could provide an economical and managerial tool to solve the traffic management decision-making problem.