engleski

Bi-Level Model For Transit Vehicle Allocation In Intermodal Transportation Networks

Network design deals with modifications in transportation networks, which seek to optimize an objective function subject to certain constraints. These modifications may be of operational or infrastructural nature. The operational changes include, among others, optimizing traffic signal settings, calculating toll patterns and changing transit settings. Infrastructural changes include adding new lanes, expanding roadway capacity, building new highways and other improvements that can change the characteristics of the transportation system. The specific changes considered in this dissertation fit under the area of operational changes and deal with the optimal allocation of a transit agency’ s vehicles to the various transit routes. Network travel patterns depend on roadway characteristics and transit level-of-service. Transit managers, in the knowledge of the travel patterns, may implement new settings that could further improve transit service. Travelers may react to the new transit settings and create a new travel pattern. The model presented herein expresses this “ game” in the form of a bi-level programming problem, where the upper level seeks the optimal transit settings, while the lower level predicts travelers’ behavior based on the decisions made in the upper level. The upper level problem is formulated as a Non-Linear Constrained Optimization problem. The behavior of travelers is formulated as a Variational Inequality problem, which accounts for the asymmetric interactions between flows on different links. A Sensitivity Analysis based algorithm is developed and implemented to solve the bi-level problem. The model is applied to a detailed intermodal commuter corridor consisting of several transit lines and transfer points that allow travelers to switch modes. The optimal resource allocation is determined, in terms of vehicles assigned to each of the transit routes. The model is a useful tool for transportation planners seeking to predict flows and costs on complex intermodal networks. It allows the evaluation of various highway and transit operating and pricing policies and strategies and considers the cross effects among transportation modes. Furthermore, the model may be used by transit agencies, seeking to determine transit settings that will help increase transit riderhsip.

bi-level; intermodal; algorithm; sensitivity-based algorithm; transit; combined model; park-and-ride; optimization; network; transportation

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