Bilevel Optimization for On-Demand Multimodal Transit Systems

TL;DR

This paper introduces a bilevel optimization framework for designing on-demand multimodal transit systems that account for rider choices and demand, improving transit network planning.

Contribution

It develops a novel bilevel model with a decomposition algorithm to optimize transit network design considering rider mode choices and demand.

Findings

Effective solution method combining Benders and combinatorial cuts

Model captures rider decision-making based on income levels

Case study demonstrates improved transit network design

Abstract

This study explores the design of an On-Demand Multimodal Transit System (ODMTS) that includes segmented mode switching models that decide whether potential riders adopt the new ODMTS or stay with their personal vehicles. It is motivated by the desire of transit agencies to design their network by taking into account both existing and latent demand, as quality of service improves. The paper presents a bilevel optimization where the leader problem designs the network and each rider has a follower problem to decide her best route through the ODMTS. The bilevel model is solved by a decomposition algorithm that combines traditional Benders cuts with combinatorial cuts to ensure the consistency of mode choices by the leader and follower problems. The approach is evaluated on a case study using historical data from Ann Arbor, Michigan, and a user choice model based on the income levels of the potential transit riders.