Abstract

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1007/978-3-319-38851-9_3 under the license http://www.springer.com/tdm
https://link.springer.com/chapter/10.1007/978-3-319-38851-9_3,
https://arxiv.org/abs/1512.09132,
https://arxiv.org/pdf/1512.09132,
https://dl.acm.org/citation.cfm?id=2954307,
https://rd.springer.com/chapter/10.1007/978-3-319-38851-9_3,
https://doi.org/10.1007/978-3-319-38851-9_3,
https://publikationen.bibliothek.kit.edu/1000068282,
https://link.springer.com/chapter/10.1007/978-3-319-38851-9_3/fulltext.html,
https://academic.microsoft.com/#/detail/2266528661
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Document information

Published on 01/01/2015

Volume 2015, 2015
DOI: 10.1007/978-3-319-38851-9_3
Licence: Other

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