Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2013 | 23 | 4 | 773-785
Tytuł artykułu

Cooperative driving at isolated intersections based on the optimal minimization of the maximum exit time

Treść / Zawartość
Warianty tytułu
Języki publikacji
Traditional traffic control systems based on traffic light have achieved a great success in reducing the average delay of vehicles or in improving the traffic capacity. The main idea of these systems is based on the optimization of the cycle time, the phase sequence, and the phase duration. The right-of-ways are assigned to vehicles of one or several movements for a specific time. With the emergence of cooperative driving, an innovative traffic control concept, Autonomous Intersection Management (AIM), has emerged. In the framework of AIM, the right-of-way is customized on the measurement of the vehicle state and the traffic control turns to determine the passing sequence of vehicles. Since each vehicle is considered individually, AIM faces a combinatorial optimization problem. This paper proposes a dynamic programming algorithm to find its optimal solution in polynomial time. Experimental results obtained by simulation show that the proper arrangement of the vehicle passing sequence can greatly improve traffic efficiency at intersections.
Opis fizyczny
  • School of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, 610054 Chengdu, China
  • Systems and Transport Laboratory, University of Technology of Belfort-Montbéliard, Rue Thierry Mieg, 90010 Belfort cedex, France
  • Systems and Transport Laboratory, University of Technology of Belfort-Montbéliard, Rue Thierry Mieg, 90010 Belfort cedex, France
  • Abbas-Turki, A., Ahmane, M., Gao, F., Wu, J., El-Moudni, A. and Miraoui, A. (2012). On the conflict with dioid algebra: Autonomous intersection management, Proceedings of the 7th IEEE Conference on Industrial Electronics and Applications, ICIEA'12, Singapore, (CD-ROM).
  • Allsop, R.E. (1971). Sigset: A computer program for calculating traffic capacity of signal-controlled road junctions, Traffic Engineering & Control (12): 58-60.
  • Allsop, R.E. (1976). Sigcap: A computer program for assessing the traffic capacity of signal-controlled road junctions, Traffic Engineering & Control (17): 338-341.
  • Arora, S., Raina, A.K. and Mittal, A.K. (2012). Collision avoidance among AGVs at junctions, Proceedings of the IEEE the Intelligent Vehicles Symposium, ICIEA'12, Singapore, pp. 585-589.
  • Bellman, R. (1957). Dynamic Programming, Princeton University Press, Princeton, NJ.
  • Bertolazzi, E., Biral, F., Lio, M.D., Saroldi, A. and Tango, F. (2009). Supporting drivers in keeping safe speed and safe distance: The SASPENCE Subproject within the European Framework Programme 6 Integrating Project PReVENT, IEEE Transactions on Intelligent Transportation Systems (99): 1-14.
  • Boillot, F., Midenet, S. and Pierrelee, J.C. (2000). Real-life cronos evaluation, Proceedings of the 10th International Conference on Road Transport Information and Control, London, UK, (CD-ROM).
  • Chen, X. Li, L. and Zhang, Y. (2010). A Markov model for headway/spacing distribution of road traffic, IEEE Transactions on Intelligent Transportation Systems 11(4): 773-785.
  • Cohen, S. (1993). Ingénierie du Trafic Routier. Eléments de thorie du trafic et applications, Presses de l'Ecole National des Ponts et Chaussées, Princeton, NJ.
  • David, F., Tsz-Chiu, A., Travis, W., Peter, S. and David, Y. (2012). Automated intersection control: Performance of a future innovation versus current traffic signal control, Transportation Research Record (2259): 223-232.
  • DiCesare, F., Kulp, P.T., Gile, M. and List, G. (1994). The application of Petri nets to the modeling, analysis and control of intelligent urban traffic networks, in R. Valette (Ed.), Application and Theory of Petri Nets, Lecture Notes in Computer Science, Vol. 815, Springer, Berlin/Heidelberg, pp. 2-15.
  • Dresner, K. and Stone, P. (2004). Multiagent traffic management: A reservation-based intersection control mechanism, Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS-2004, New York, NY, USA, pp. 530-537.
  • Dresner, K. and Stone, P. (2006). Traffic intersections of the future, Proceedings of the 21st National Conference on Artificial Intelligence, AAAI-2006, Boston, MA, USA, pp. 1593-1596.
  • Dresner, K. and Stone, P. (2008). A multiagent approach to autonomous intersection management, Journal of Artificial Intelligence Research 31(13): 591-656.
  • Fang, F.C. and Elefteriadou, L. (2006). Development of an optimization methodology for adaptive traffic signal control at diamond interchanges, Journal of Transportation Engineering 132(8): 629-637.
  • Gartner, N. (1983). OPAC: A demand-responsive strategy for traffic signal control, Transportation Research Record (906): 75-81.
  • Gipps, P.G. (1986). MULTSIM: A model for simulating vehicular traffic on multi-lane arterial roads, Mathematics and Computers in Simulation 28(4): 291-295.
  • Gipps, P.G. (1981). A behavioural car-following model for computer simulation, Transportation Research, Part B: Methodological 15(2): 105-111.
  • Gradinescu, V., Gorgorin, C., Diaconescu, R., Cristea, V. and Iftode, L. (2007). Adaptive traffic lights using car-to-car communication, Proceedings of the 65th IEEE Vehicular Technology Conference, VTC2007-Spring, Dublin, Ireland, pp. 21-25.
  • Grünewald, M., Rust, C. and Witkowski, U. (2006). Using mini robots for prototyping intersection management of vehicles, Proceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment AMIRE 2005, Awara-Spa, Fukui, Japan, pp. 287-292.
  • Henry, J.J., Farges, J.L., and Tuffal, J. (1983). The PRODYN real-time traffic algorithm, Proceedings of the 4th IFAC Symposium on Transportation Systems, Baden Baden, Germany, pp. 307-312.
  • Hunt, P.B. (1982). The scoot on-line traffic signal optimisation technique, Traffic Engineering & Control (23): 190-192.
  • Kutz, M. (2004). Handbook of Transportation Engineering, McGraw-Hill, Boca Raton, FL.
  • Lee, J. and Hyung, L. (1999). Distributed and cooperative fuzzy controllers for traffic intersections group, IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews 29(2): 263-271.
  • Li, L. and Wang, F.Y. (2006). Cooperative driving at blind crossings using intervehicle communication, IEEE Transactions on Vehicular Technology 55(6): 1712-1724.
  • Little, J.D.C. (1961). A proof for the queuing formula: L = λw, Operation Research 9(3): 383-387.
  • Matteo, V. and Sascha, O. (2009). Evaluating policies for reservation-based intersection control, Proceedings of the 14th Portuguese Conference on Artificial Intelligence, EPIA'09, Aveiro, Portugal, pp. 21-25.
  • Mehani, O. and de La Fortelle, A. (2007). Trajectory planning in a crossroads for a fleet of driverless vehicles, Computer Aided Systems Theory (4739): 1159-1166.
  • Mirchandani, P. and Lucas, D.E. (2001). RHODES-ITMS Tempe field test project: Implementation and field testing of RHODES, a real-time traffic adaptive control system, Technical Report FHWA-AZ01-447, Federal Highway Administration, Washington, DC.
  • Mirchandani, P. and Head, L. (2001). A real-time traffic signal control system: Architecture, algorithms, and analysis, Transportation Research, Part C: Emerging Technologies 9(6): 415-432.
  • Papageorgiou, M., Diakaki, P., Kotsialos, D. and Wang, Y.B (2003). Review of road traffic control strategies, Proceedings of the IEEE 91(12): 2043-2067.
  • Péter, T. (2012). Modeling nonlinear road traffic networks for junction control, International Journal of Applied Mathematics and Computer Science 22(3): 723-732, DOI: 10.2478/v10006-012-0054-1.
  • Potts, C.N. and Kovalyov, M.Y. (2000). Scheduling with batching: A review, European Journal of Operational Research 120(2): 228-249.
  • Robertson, D.I. (1969). Transyt method for area traffic control, Traffic Engineering & Control (10): 276-281.
  • Sakaguchi, T., Uno, A., Kato, and Tsugawa, S. (2000). Cooperative driving of automated vehicles with inter-vehicle communications, Proceedings of the IEEE Intelligent Vehicles Symposium, IV 2000, Dearborn, MI, USA, pp. 516-521.
  • Sen, S. and Head, K.L. (1997). Controlled optimization of phases at an intersection, Transportation Science 31(1): 5-17.
  • Sims, A.G. and Dobinson, K.W. (1980). The Sydney Coordinated Adaptive Traffic (SCAT) system philosophy and benefits, IEEE Transactions on Vehicular Technology 29(2): 130-137.
  • Thomas, H.C., Leiserson, C.E., Rivest, R.L. and Stein, C. (2009). Introduction to Algorithms, MIT Press, Cambridge, MA.
  • Tsugawa, S. (2002). Inter-vehicle communications and their applications to intelligent vehicles: An overview, Proceedings of the IEEE 2002 Intelligent Vehicle Symposium, Versailles, France, pp. 564-569.
  • Uno, A., Sakaguchi, T. and Tsugawa, S. (1999). A merging control algorithm based on inter-vehicle communication, Proceedings of the IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems 1999, Tokyo, Japan, pp. 783-787.
  • Webster, F.V. (1958). Road research technical paper, Technical Report 39, Road Research Laboratory, London.
  • Wunderlich, R., Liu, C.B., Elhanany, I. and Urbanik, T. (2008). A novel signal-scheduling algorithm with quality-of-service provisioning for an isolated intersection, IEEE Transactions on Intelligent Transportation Systems 9(3): 536-547.
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.