Multimodal Dynamic Journey Planning

TL;DR

This paper introduces a multimodal dynamic timetable model (DTM) for real-time, efficient journey planning across public transport and other modes, demonstrating superior performance on real-world networks.

Contribution

It extends the existing DTM to handle multimodal transportation, enabling fast queries and updates for complex, real-world journey planning scenarios.

Findings

Favorable comparison with state-of-the-art methods

Supports multimodal transportation including walking and electric vehicles

Achieves real-time query and update performance

Abstract

We present multimodal DTM, a new model for multimodal journey planning in public (schedule-based) transport networks. Multimodal DTM constitutes an extension of the dynamic timetable model (DTM), developed originally for unimodal journey planning. Multimodal DTM exhibits a very fast query algorithm, meeting the request for real-time response to best journey queries and an extremely fast update algorithm for updating the timetable information in case of delays. In particular, an experimental study on real-world metropolitan networks demonstrates that our methods compare favorably with other state-of-the-art approaches when public transport along with unrestricted w.r.t. departing time traveling (walking and electric vehicles) is considered.