Open Access
Issue
MATEC Web Conf.
Volume 220, 2018
2018 The 2nd International Conference on Mechanical, System and Control Engineering (ICMSC 2018)
Article Number 02004
Number of page(s) 6
Section Vehicle Design and Manufacturing Engineering
DOI https://doi.org/10.1051/matecconf/201822002004
Published online 29 October 2018
  1. A. Eskandarian, Handbook of intelligent vehicles, vol. 1-2, (2012) [CrossRef] [Google Scholar]
  2. D. Miculescu and S. Karaman, “Polling-systems based control of high-performance provably-safe autonomous intersections,” vol. 2015- February, no. February, pp. 1417-1423, (2014) [Google Scholar]
  3. F. Zhou, X. Li, and J. Ma, “Parsimonious shooting heuristic for trajectory design of connected automated traffic part i: Theoretical analysis with generalized time geography,” Transportation Research Part B: Methodological, vol. 95, pp. 394-420, (2017). [CrossRef] [Google Scholar]
  4. P. Varaiya, “Smart cars on smart roads: Problems of control,” IEEE Transactions on Automatic Control, vol. 38, no. 2, pp. 195–207, (1993). [CrossRef] [Google Scholar]
  5. B. Paden, M. Cap, S. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” CoRR, vol. abs/1604.07446, (2016). [Google Scholar]
  6. E. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, vol. 1, no. 1, pp. 269–271, (1959). [CrossRef] [MathSciNet] [Google Scholar]
  7. P. Hart, N. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100–107, (1968). [CrossRef] [Google Scholar]
  8. A. Goldberg and . Harrelson, “Computing the shortest path: A search meets graph theory,” in Proceedings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms, ser. SODA ’05. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics, pp. 156-165, (2005) [Google Scholar]
  9. R. Geisberger, P. Sanders, D. Schultes, and C. Vetter, “Exact routing in large road networks using contraction hierarchies,” Transportation Science, vol. 46, no. 3, pp. 388–404, (2012). [CrossRef] [Google Scholar]
  10. H. Bast, D. Delling, A. Goldberg, M. Mller Hannemann, T. Pajor, P. Sanders, D. Wagner, and R. Werneck, “Route planning in transportation networks,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9220 LNCS, pp. 19-80, (2016). [Google Scholar]
  11. S. Colak, A. Lima, and M. Gonzlez, “Understanding congested travel in urban areas,” Nature Communications, vol. 7, (2016). [CrossRef] [Google Scholar]
  12. E. Schmitt and H. Jula, “Vehicle route guidance systems: Classification and comparison,” pp. 242-247, (2006) [Google Scholar]
  13. P. Desai, S. Loke, A. Desai, and J. Singh, “Multi agent based vehicular congestion management,” pp. 1031-1036, (2011) [Google Scholar]
  14. S. Wang, S. Djahel, and J. McManis, “A multi-agent based vehicles rerouting system for unexpected traffic congestion avoidance,” pp. 2541-2548, (2014) [Google Scholar]
  15. K. Dresner and P. Stone, “Sharing the road: Autonomous vehicles meet human drivers,” pp. 1263-1268, (2007) [Google Scholar]
  16. R. Kanamori, J. Takahashi, and T. Ito, “A study of route assignment strategy based on anticipatory stigmergy,” Electronics and Communications in Japan, vol. 99, no. 3, pp. 1645–1651, (2016). [CrossRef] [Google Scholar]
  17. J. Wardrop, “Some theoretical aspects of road traffic research,” Proceedings of the Institution of Civil Engineers, vol. 1, no. 3, pp. 325–362, (1952). [CrossRef] [Google Scholar]
  18. M. Hasan, A. Bazzan, E. Friedman, and A. Raja, “A multiagent solution to overcome selfish routing in transportation networks,” pp. 1850-1855, (2016) [Google Scholar]
  19. O. Jahn, R. Mhring, A. Schulz, and N. Stier-Moses, “System-optimal routing of traffic flows with user constraints in networks with congestion,” Operations Research, vol. 53, no. 4, pp. 600–616, (2005). [CrossRef] [Google Scholar]
  20. N. Groot, B. De Schutter, and H. Hellendoorn, “Toward system-optimal routing in traffic networks: A reverse stackelberg game approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 1, pp. 29–40, (2015). [CrossRef] [Google Scholar]
  21. C. Menelaou, P. Kolios, S. Timotheou, C. Panayiotou, and M. Polycarpou, “Controlling road congestion via a low-complexity route reservation approach,” Transportation Research Part C: Emerging Technologies, vol. 81, pp. 118–136, (2017). [CrossRef] [Google Scholar]
  22. A. Agafonov and V. Myasnikov, “Efficiency comparison of the routing algorithms used in centralized traffic management systems,” vol. 201, pp. 265-270, (2017) [Google Scholar]
  23. K. Saw, B. Katti, and G. Joshi, “Literature review of traffic assignment: Static and dynamic,” International Journal of Transportation Engineering, vol. 2, no. 4, pp. 339–347, (2015). [Google Scholar]
  24. J. Li and Q.-Y. Chen, “Speed-density relationship: From deterministic to stochastic,” The 88th Transportation Research Board (TRB) Annual Meeting, pp. 1-20, 01, (2009). [Google Scholar]
  25. Trans Res Board, Highway Capacity Manual. Washington, D.C.: Transportation Research Board, National Research Council, (2000). [Google Scholar]
  26. A. Chakirov and P. Fourie, Enriched Sioux Falls Scenario with Dynamic and Disaggregate Demand. Singapore ETH Centre (SEC): Future Cities Laboratory, (2014). [Google Scholar]
  27. D. Krajzewicz, J. Erdmann, M. Behrisch, and L. Bieker, “Recent development and applications of SUMO - Simulation of Urban MObility,” International Journal On Advances in Systems and Measurements, vol. 5, no. 3&4, pp. 128-138, December, (2012). [Google Scholar]
  28. S. Krauss, P. Wagner, and C. Gawron, “Metastable states in a microscopic model of traffic flow,” Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, vol. 55, no. 5, pp. 5597–5602, (1997). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.