Network Design with Elastic Demand and Dynamic Passenger Assignment to Assess the Performance of Transit Services
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 5
Abstract
This study proposes a solution framework for operational analysis and financial assessment of transit services that considers the passenger behavior and the elasticity of transit demand to service characteristics. The proposed solution framework integrates a dynamic transit passenger assignment model (Fast-Trips) with a mode choice model and a service design module, and iterates these methods until an equilibrium between fares and frequencies is reached. The solution framework was implemented for a newly conceived intercity transit service in Arizona, and the system performance was studied for multiple fare policy and frequency design scenarios. The results showed that the scenarios with designed-oriented frequencies had lower ratios of revenue to operating cost () compared with those in which frequencies were set based on the passenger path-choice behaviors and route usage, which emphasizes the importance of considering elastic transit demand in network and service designs. The sensitivity analysis also indicated that there are multiple ways to achieve a certain ratio, and therefore it is the other objectives and the operator’s priorities that define the final design and service characteristics.
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Data Availability Statement
Some data, models, and code used during the study were provided by a third party (total demand OD matrix, statewide network graph, and Fast-Trips model). Direct requests for these materials may be made to the provider as indicated in the Acknowledgements. Some data, models, and code generated or used during the study are available from the corresponding author by request (mode choice model, Flexpress GTFS-Plus and Dyno-Demand files, and conversion scripts for Fast-Trips input/output files).
Acknowledgments
The authors thank Clint Daniels, Elizabeth Sall, and Lisa Zorn from the Fast-Trips implementation project team for their help with running and debugging Fast-Trips and moving this research ahead. The authors also thank Carlos Lopez and Baloka Belezamo from the Arizona Department of Transportation for providing the GIS shapefiles and demand data for the Arizona statewide network.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 5 • May 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Feb 4, 2019
Accepted: Sep 4, 2019
Published online: Feb 29, 2020
Published in print: May 1, 2020
Discussion open until: Jul 29, 2020
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