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

TL;DR

This paper addresses multimodal trip planning by integrating PT, ride-pooling, and micro-mobility within a dynamic, preference-aware framework. It formulates a mixed-integer program that embeds passenger preferences into the objective and proposes a rolling-horizon meta-heuristic (ALNS for ride-pooling and a heuristic for micro-mobility) to manage dynamic requests. The approach leverages GTFS data and real-world Rotterdam-area inputs to show near-optimal performance and derive managerial insights on modal shares, pricing, and smartphone-accessible mobility. Practically, the framework supports more sustainable urban mobility by aligning service provision with heterogeneous user preferences and dynamic resource availability, while offering scalable tooling for MaaS platforms. The work also outlines future directions in demand forecasting, dynamic pricing, and adaptive re-balancing to further enhance system efficiency and equity.

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.

Figures (11)

• Figure 1: Interaction between passengers and a mobility platform
• Figure 2: Dynamic multimodal trip planning based on rolling horizon.
• Figure 3: Multi-modal trip planning model.
• Figure 4: Requests and vehicles in Maassluis area.
• Figure 5: Comparison of the proposed heuristic algorithm and a greedy approach.