An adaptive route choice model for integrated fixed and flexible transit systems

Abstract

Over the past decade, there has been a surge of interest in the application of agent-based simulation models to evaluate flexible transit solutions characterized by different degrees of short-term flexibility in routing and scheduling. A central modelling decision in the development is how one chooses to represent the mode- and route-choices of travellers. The real-time adaptive behaviour of travellers is important to model in the presence of a flexible transit service, where the routing and scheduling of vehicles is highly dependent on supply-demand dynamics at a near real-time temporal resolution. We propose a utility-based transit route-choice model with representation of within-day adaptive travel behaviour and between-day learning where station-based fixed-transit, flexible-transit, and active-mode alternatives may be dynamically combined in a single path. To enable experimentation, this route-choice model is implemented within an agent-based dynamic public transit simulation framework. We first explore model properties in a choice between fixed- and flexible-transit modes for a toy network. The adaptive route choice framework is then applied to a case study based on a real-life branched transit service in Stockholm, Sweden. This case study illustrates level-of-service trade-offs, in terms of waiting times and in-vehicle times, between passenger groups and analyzes traveller mode choices within a mixed fixed- and flexible transit system. Results show that the proposed framework is capable of capturing dynamic route choices in mixed flexible and fixed transit systems and that the day-to-day learning model leads to stable fixed-flexible mode choices.

Keywords:

• Public transit
• flexible transit
• agent-based simulation
• transit assignment
• route choice

Acknowledgments

The authors would like to thank Matej Cebecauer for his help and instructive advice in acquiring the demand data used in the second case study and Giorgos Laskaris for sharing the underlying network and fixed transit operations data used in the second case study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This paper is part of the SMART (Simulation and Modeling of Autonomous Road Transport) project (grant number TRV-2019/27044), financed by the Swedish Transport Administration Trafikverket. The work of the third author was supported also by the CriticalMaaS project (grant number 804469), which is financed by the European Research Council and the Amsterdam Institute for Advanced Metropolitan Solutions.