License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.COSIT.2017.17
URN: urn:nbn:de:0030-drops-77615
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2017/7761/
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Liebig, Thomas ; Sotzny, Maurice

On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning

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LIPIcs-COSIT-2017-17.pdf (0.9 MB)

Abstract

Urban areas are increasingly subject to congestions. Most navigation systems and algorithms that avoid these congestions consider drivers independently and can, thus, cause novel congestions at unexpected places. Pre-computation of optimal trips (Nash equilibrium) could be a solution to the problem but is due to its static nature of no practical relevance.

In contrast, the paper at-hand provides an approach to avoid traffic jams with dynamic self-organizing trip planning. We apply reinforcement learning to learn dynamic weights for routing from the decisions and feedback logs of the vehicles. In order to compare our routing regime against others, we validate our approach in an open simulation environment (LuST) that allows reproduction of the traffic in Luxembourg for a particular day. Additionally, in two realistic scenarios: (1) usage of stationary sensors and (2) deployment in a mobile navigation system, we perform experiments with varying penetration rates. All our experiments reveal that performance of the traffic network is increased and occurrence of traffic jams are reduced by application of our routing regime.

BibTeX - Entry

@InProceedings{liebig_et_al:LIPIcs:2017:7761,
  author =	{Thomas Liebig and Maurice Sotzny},
  title =	{{On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning}},
  booktitle =	{13th International Conference on Spatial Information Theory (COSIT 2017)},
  pages =	{17:1--17:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-043-9},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{86},
  editor =	{Eliseo Clementini and Maureen Donnelly and May Yuan and Christian Kray and Paolo Fogliaroni and Andrea Ballatore},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7761},
  URN =		{urn:nbn:de:0030-drops-77615},
  doi =		{10.4230/LIPIcs.COSIT.2017.17},
  annote =	{Keywords: situation-aware trip planning, self-organizing traffic, reinforcement learning}
}

Keywords: situation-aware trip planning, self-organizing traffic, reinforcement learning
Collection: 13th International Conference on Spatial Information Theory (COSIT 2017)
Issue Date: 2017
Date of publication: 29.08.2017

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