Efficient Timetable Information in the Presence of Delays
The search for train connections in state-of-the-art commercial timetable information systems is based on a static schedule. Unfortunately, public transportation systems suffer from delays for various reasons. Thus, dynamic changes of the planned schedule have to be taken into account. A system that has access to delay information about trains (and uses this information within search queries) can provide valid alternatives in case a connection does not work. Additionally, it can be used to actively guide passengers as these alternatives may be presented before the passenger is already stranded at a station due to an invalid transfer.
In this work, we present an approach which takes a stream of delay information and schedule changes on short notice (partial train cancellations, extra trains) into account. Primary delays of trains may cause a cascade of so-called secondary delays of other trains which have to wait according to certain policies for delays between connecting trains. We introduce the concept of a dependency graph to efficiently calculate and update all primary and secondary delays. This delay information is then incorporated into a time-expanded search graph which has to be updated dynamically. These update operations are quite complex, but turn out to be not time-critical in a fully realistic scenario.
We finally present a case study with data provided by Deutsche Bahn AG, showing that this approach has been successfully integrated into the multi-criteria timetable information system MOTIS and can handle massive delay data streams instantly.
Keywordstimetable information system primary and secondary delays dependency graph dynamic graph update
Unable to display preview. Download preview PDF.
- 1.Frede, L., Müller-Hannemann, M., Schnee, M.: Efficient on-trip timetable information in the presence of delays. In: Fischetti, M., Widmayer, P. (eds.) Proceedings of ATMOS 2008 - 8th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany (2008)Google Scholar
- 3.Delling, D., Giannakopoulou, K., Wagner, D., Zaroliagis, C.: Timetable Information Updating in Case of Delays: Modeling Issues. Technical report ARRIVAL-TR-0133, ARRIVAL Project (2008)Google Scholar
- 5.Gatto, M., Glaus, B., Jacob, R., Peeters, L., Widmayer, P.: Railway delay management: Exploring its algorithmic complexity. In: Hagerup, T., Katajainen, J. (eds.) SWAT 2004. LNCS, vol. 3111, pp. 199–211. Springer, Heidelberg (2004)Google Scholar
- 10.Anderegg, L., Penna, P., Widmayer, P.: Online train disposition: to wait or not to wait? ATMOS 2002. In: ICALP 2002 Satellite Workshop on Algorithmic Methods and Models for Optimization of Railways, Electronic Notes in Theoretical Computer Science, vol. 66 (2002)Google Scholar
- 11.Müller-Hannemann, M., Schnee, M.: Paying less for train connections with MOTIS. In: Kroon, L.G., Möhring, R.H. (eds.) 5th Workshop on Algorithmic Methods and Models for Optimization of Railways, Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany (2006)Google Scholar
- 12.Gunkel, T., Müller-Hannemann, M., Schnee, M.: Improved search for night train connections. In: Liebchen, C., Ahuja, R.K., Mesa, J.A. (eds.) Proceedings of ATMOS 2007 - 7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems, Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany (2007); Extended journal version appears in NetworksGoogle Scholar