Robust Routing, Assignment, and Simulation of Transit Systems

University Transportation Centers Program2018PDFTech ReportLownes, NicholasUniversity of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and EducationUniversity of Connecticut. Department of Civil and Environmental EngineeringUnited States. Department of Transportation. University Transportation Centers (UTC) ProgramUnited States. Department of Transportation. Office of the Assistant Secretary for Research and TechnologyUniversity of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and EducationConnecticutUnited StatesDecision makingPublic transitQuality of serviceRidershipRoutingSimulationTransit operating agenciesTravel patternsMultimodal networksStochastic modelsTransit assignmentUser behaviorProject ID: 2017 Project 0669A3551747133FinalTransit system complexity is a function not only just of the infrastructure but also is strongly tied to user behavior, which is driven by perception of the quality of service (in experiencing waiting, transfers, and travel time variability), which is not always an accurate reflection of the true quality of service. The variability and inconsistency (with reality) of these perceptions can be captured in part by correlating the multiple data sources, observing travel patterns and modeling user behavior under uncertainty. The effect of service reliability and accessibility will be investigated towards the development of route choice, stop choice, and departure time choice models. Stochastic multimodal network and user behavior models will be leveraged toward the development of routing, assignment, and simulation models for ridership estimation and performance analysis. Robust analysis tools will be developed that take into account system characteristics which are a function of the performance of the auto network, models passenger flow in the network under stochastic rules and predicts system wide travel patterns. Such models will enhance decisions made by transit agencies when allocating resources toward additional capacity, schedule updates, and stop/station location in the long term; and stop skipping, rerouting, and vehicle holding as real-time operational decisions. The objective of this project is to (1) represent the transit users\u2019 behavior of Hartford, Connecticut more accurately; (2) model the transit system with high resolution; and (3) produce disaggregate results. To accomplish this project, the research team will work on developing the input network dataset and passenger demand data for Hartford, Connecticut. An open source code-base dynamic transit assignment tool then will be used to simulate transit passenger route-finding and user experiences.143