Dynamic Preference-based Multi-modal Trip Planning of Public Transport and Shared Mobility

TL;DR

This paper presents a preference-based optimization framework for multi-modal trip planning that integrates public transport, ride-pooling, and shared micro-mobility, aiming to reduce emissions and improve urban mobility.

Contribution

It introduces a novel mixed-integer programming model combined with a meta-heuristic algorithm for dynamic, preference-aware multi-modal trip planning.

Findings

The algorithm efficiently finds near-optimal solutions in real-world scenarios.

It provides managerial insights across different passenger segments and mobility options.

The approach effectively manages dynamic requests with a rolling horizon method.

Abstract

The shift from private vehicles to public and shared transport is crucial to reducing emissions and meeting climate targets. Consequently, there is an urgent need to develop a multimodal transport trip planning approach that integrates public transport and shared mobility solutions, offering viable alternatives to private vehicle use. To this end, we propose a preference-based optimization framework for multi-modal trip planning with public transport, ride-pooling services, and shared micro-mobility fleets. We introduce a mixed-integer programming model that incorporates preferences into the objective function of the mathematical model. We present a meta-heuristic framework that incorporates a customized Adaptive Large Neighborhood Search algorithm and other tailored algorithms, to effectively manage dynamic requests through a rolling horizon approach. Numerical experiments are conducted using real transport network data in a suburban area of Rotterdam, the Netherlands. Model application results demonstrate that the proposed algorithm can efficiently obtain near-optimal solutions. Managerial insights are gained from comprehensive experiments that consider various passenger segments, costs of micro-mobility vehicles, and availability fluctuation of shared mobility.