Models and Solution Algorithms for Transit and Intermodal Passenger Assignment (Development of FAST-TrIPs Model)
AdvisorHickman, Mark D.
Committee ChairHickman, Mark D.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractIn this study, a comprehensive set of transit, intermodal and multimodal assignment models (FAST-TrIPs) is developed for transportation planning and operations purposes. The core part of the models is a schedule-based transit assignment with capacity constraint and boarding priority. The problem is defined to model the system performance dynamically by taking into account the scheduled transit service and to model user behavior more realistically by taking into account capacity of transit vehicles and boarding priority for passengers arriving early to a stop or a transfer point. An optimization model is proposed for both deterministic and stochastic models, which includes a penalty term in the objective function to model the boarding priority constraint. The stochastic model is proposed based on logit route choice with flexibility on the level of stochasticity in route choice. Optimality conditions show that the models are consistent with network equilibrium and user behavior. An extension of the optimization models is proposed for multimodal assignment problem, in which the transit and auto networks interact dynamically. To solve the proposed models, since the penalty term is non-linear and makes the model an asymmetric nonlinear optimization model with side constraints, a simulation-based approach is developed. The solution method incorporates the path assignment models and a mesoscopic transit passenger simulation in conjunction with Dynamic Traffic Assignment (DTA) models. The simulation model can capture detailed activities of transit passengers and determines the nonlinear penalty explicitly by reporting passengers' failure to board experience. Therefore, the main problem can be solved iteratively, by solving a relaxed problem and simulating the transit network in each iteration, until the convergence criterion is met. The relaxed problem is a path generation model and can be either a shortest/least-cost path or a logit-based hyperpath in the schedule-based transit network. An efficient set of path models are developed using Google's General Transit Feed Specification (GTFS) files, taking into account the transit network hierarchy for computational efficiency of the model. A multimodal assignment model is also developed by integration of the proposed transit assignment model with DTA models. The model is based on simulation and is able to capture the effect of transit and auto mode on each other through an iterative solution method and feedback loop from the transit assignment model to the DTA models. In the multimodal assignment model, more realistic transit vehicle trajectories are generated in the DTA models and are used for assigning transit passengers to transit vehicles. In an application of the multimodal assignment, intermodal tours are modeled considering the timing of auto trips and transit connections, the schedule-based transit network, and the constraint on park-n-ride choice in a tour. The model can simulate the transit, auto, and intermodal tours together with high resolution and realistic user behavior.
Degree ProgramGraduate College