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On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning

Authors Thomas Liebig, Maurice Sotzny



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Thomas Liebig
Maurice Sotzny

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Thomas Liebig and Maurice Sotzny. On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning. In 13th International Conference on Spatial Information Theory (COSIT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 86, pp. 17:1-17:12, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/LIPIcs.COSIT.2017.17

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.
Keywords
  • situation-aware trip planning
  • self-organizing traffic
  • reinforcement learning

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References

  1. Jean-Yves Audibert and Rémi Munos. Introduction to bandits: Algorithms and theory. ICML Tutorial on bandits, 2011. Google Scholar
  2. Harideo Chaudhary. Application of the theory of a single first order equation to traffic flow. Journal of the Institute of Engineering, 9(1):175-181, 2014. Google Scholar
  3. Lara Codeca, Raphaël Frank, and Thomas Engel. Luxembourg SUMO traffic (LuST) scenario: 24 hours of mobility for vehicular networking research. In Vehicular Networking Conference (VNC), 2015 IEEE, pages 1-8. IEEE, 2015. Google Scholar
  4. Serdar Çolak, Antonio Lima, and Marta C González. Understanding congested travel in urban areas. Nature communications, 7, 2016. Google Scholar
  5. Edsger W. Dijkstra. A note on two problems in connexion with graphs. Numerische mathematik, 1(1):269-271, 1959. Google Scholar
  6. Robert Geisberger, Peter Sanders, Dominik Schultes, and Daniel Delling. Contraction hierarchies: Faster and simpler hierarchical routing in road networks. In International Workshop on Experimental and Efficient Algorithms, pages 319-333. Springer, 2008. Google Scholar
  7. F. Harary and R. Norman. Some properties of line digraphs. Rendiconti del Circolo Matematico di Palermo, 9(2):161-168, May 1960. URL: http://dx.doi.org/10.1007/BF02854581.
  8. Peter E Hart, Nils J Nilsson, and Bertram Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics, 4(2):100-107, 1968. Google Scholar
  9. S. P. Hoogendoorn, P. H. L. Bovy, and W. Daamen. Microscopic Pedestrian Wayfinding and Dynamics Modelling. In M. Schreckenberg and S. D. Sharma, editors, Pedestrian and Evacuation Dynamics, pages 123-155, 2002. Google Scholar
  10. Dermot Kinane, François Schnitzler, Shie Mannor, Thomas Liebig, Katharina Morik, Jakub Marecek, Bernard Gorman, Nikolaos Zygouras, Yannis Katakis, Vana Kalogeraki, et al. Intelligent synthesis and real-time response using massive streaming of heterogeneous data (insight) and its anticipated effect on intelligent transport systems (its) in dublin city, ireland. ITS, Dresden, Germany, 2014. Google Scholar
  11. Daniel Krajzewicz, Jakob Erdmann, Michael Behrisch, and Laura Bieker. Recent development and applications of SUMO - Simulation of Urban MObility. International Journal On Advances in Systems and Measurements, 5(3&4):128-138, December 2012. Google Scholar
  12. Thomas Liebig. Privacy preserving centralized counting of moving objects. In Fernando Bacao, Maribel Yasmina Santos, and Marco Painho, editors, AGILE 2015, Lecture Notes in Geoinformation and Cartography, pages 91-103. Springer International Publishing, 2015. URL: http://dx.doi.org/10.1007/978-3-319-16787-9_6.
  13. Thomas Liebig, Nico Piatkowski, Christian Bockermann, and Katharina Morik. Dynamic route planning with real-time traffic predictions. Information Systems, 2016. Google Scholar
  14. Ruilin Liu, Hongzhang Liu, Daehan Kwak, Yong Xiang, Cristian Borcea, Badri Nath, and Liviu Iftode. Themis: A participatory navigation system for balanced traffic routing. In 2014 IEEE Vehicular Networking Conference (VNC), pages 159-166. IEEE, 2014. Google Scholar
  15. Jakub Mareček, Robert Shorten, and Jia Yuan Yu. Signalling and obfuscation for congestion control. International Journal of Control, 88(10):2086-2096, 2015. Google Scholar
  16. Jakub Mareček, Robert Shorten, and Jia Yuan Yu. r-extreme signalling for congestion control. International Journal of Control, pages 1-13, 2016. Google Scholar
  17. R. McGill, J. W. Tukey, and W. A. Larsen. Variations of Box Plots. The American Statistician, 32(1):12-16, 1978. Google Scholar
  18. John Nash. Non-cooperative games. Annals of mathematics, pages 286-295, 1951. Google Scholar
  19. Tim Roughgarden and Éva Tardos. How bad is selfish routing? Journal of the ACM (JACM), 49(2):236-259, 2002. Google Scholar
  20. Marco Stolpe, Thomas Liebig, and Katharina Morik. Communication-efficient learning of traffic flow in a network of wireless presence sensors. In Proceedings of the Workshop on Parallel and Distributed Computing for Knowledge Discovery in Data Bases (PDCKDD 2015), CEUR Workshop Proceedings, page (to appear). CEUR-WS, 2015. Google Scholar
  21. Adith Swaminathan and Thorsten Joachims. Batch learning from logged bandit feedback through counterfactual risk minimization. Journal of Machine Learning Research, 16:1731-1755, 2015. Google Scholar
  22. Remi Tachet, Paolo Santi, Stanislav Sobolevsky, Luis Ignacio Reyes-Castro, Emilio Frazzoli, Dirk Helbing, and Carlo Ratti. Revisiting street intersections using slot-based systems. PloS one, 11(3):e0149607, 2016. Google Scholar
  23. J. W. Tukey. Exploratory Data Analysis. Number 1 in Exploratory Data Analysis. Addison Wesley Publishing Company, 1970. Google Scholar
  24. Sandesh Uppoor, Oscar Trullols-Cruces, Marco Fiore, and Jose M. Barcelo-Ordinas. Generation and analysis of a large-scale urban vehicular mobility dataset. IEEE Transactions on Mobile Computing, 13(5):1061-1075, 2014. Google Scholar
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