DeepAI AI Chat
Log In Sign Up

Extending the Time Horizon: Efficient Public Transit Routing on Arbitrary-Length Timetables

by   Sascha Witt, et al.

We study the problem of computing all Pareto-optimal journeys in a public transit network regarding the two criteria of arrival time and number of transfers taken. In recent years, great advances have been made in making public transit network routing more scalable to larger networks. However, most approaches are silent on scalability in another dimension: Time. Experimental evaluations are often done on slices of timetables spanning a couple of days, when in reality, the planning horizon is much longer. We introduce an extension to trip-based public transit routing, proposed in [12], that allows efficient handling of arbitrarily long timetables. Our experimental evaluation shows that the resulting algorithm achieves fast queries on year-spanning timetables, and can incorporate updates such as delays or changed routes quickly even on large networks.


page 1

page 5

page 7

page 9

page 10


Fast Public Transit Routing with Unrestricted Walking through Hub Labeling

We propose a novel technique for answering routing queries in public tra...

Fast Multimodal Journey Planning for Three Criteria

We study the journey planning problem for multimodal networks consisting...

UnLimited TRAnsfers for Multi-Modal Route Planning: An Efficient Solution

We study a multi-modal route planning scenario consisting of a public tr...

Arc-Flags Meet Trip-Based Public Transit Routing

We present Arc-Flag TB, a journey planning algorithm for public transit ...

Price Optimal Routing in Public Transportation

With the development of fast routing algorithms for public transit the o...

Multimodal Dynamic Journey Planning

We present multimodal DTM, a new model for multimodal journey planning i...