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Green wave optimization

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Języki publikacji
EN
Abstrakty
EN
In this paper we present the results of global optimization of green wave parameters (offsets, opening times, speed limit) for the main artery in the city of Wrocław (Poland). The optimization process was performed in ArsNumerica Execution Environment [1] and involved two different objective functions: the average waiting time and the average queue length. Both approaches were compared by calculating the number of vehicles that pssed the artery in a prescribed time.
Rocznik
Strony
3--8
Opis fizyczny
Bibliogr. 22 poz.
Twórcy
autor
  • Wrocław University of Technology, Faculty of Electronics, Department of Computer Engineering, ul. Janiczewskiego 11/17, 50-372 Wrocław, Poland
autor
  • Wrocław University of Technology, Faculty of Electronics, Department of Computer Engineering, ul. Janiczewskiego 11/17, 50-372 Wrocław, Poland
autor
  • Wrocław University of Technology, Faculty of Electronics, Department of Computer Engineering, ul. Janiczewskiego 11/17, 50-372 Wrocław, Poland
autor
  • Wrocław University of Technology, Faculty of Electronics, Department of Control Systems and Mechatronics, ul. Janiczewskiego 11/17, 50-372 Wrocław, Poland
autor
  • Wrocław University of Technology, Faculty of Electronics, Department of Computer Engineering, ul. Janiczewskiego 11/17, 50-372 Wrocław, Poland
autor
  • Wrocław University of Technology, Faculty of Electronics, Department of Computer Engineering, ul. Janiczewskiego 11/17, 50-372 Wrocław, Poland
Bibliografia
  • [1] BAZAN M., et al.: Intelligent Transport System auditing using road traffi c micro-simulation, in Mikulski J. (ed) Tools of Transport Telematics, Springer Verlag, Berlin Heidelberg, CCIS 531, 2015.
  • [2] CEYLAN H.: Developing combined genetic algorithm – hill climbing optimization method for area traffi c control. Journal of Transportation Engineering, 132(8): 663 – 671, 2006.
  • [3] CEYLAN H., BELL G.H.M.: Genetic algorithm solution for the stochastic equilibrium transportation networks under congestion. Transportation Research Part B, 39:169 – 185, 2004b.
  • [4] CAO C. T., CUI F., GUO . Q.: “Two-Direction Green Wave Control of Traffic Signal Based on Particle Swarm Optimization”, Applied Mechanics and Materials, Vols. 26-28, pp. 507-511, Jun. 2010.
  • [5] GARTNER N.H., STAMATIADIS C.: Arterial-based control of Traffic Flow in Urban Grid Networks, Mathematical and Computer Modelling (35), pp. 657-671, 2002.
  • [6] GUBERINIĆ S., ŚENBORN G., LAZIĆ B.: Optimal Traffi 1c Control, Urban Intersections, CRC Press, 2008.
  • [7] GUO L., YANG R., ZHAN M.: Arterial Traffic Two-direction Green Wave Coordination Control Based on MATLAB Graphical Method, 2015 2nd International Conference on Information Science and Control Engineering, 2015.
  • [8] KRA JZEWICZ, D., et al. : Simulation of modern Traffi c Lights Control Systems using the open source Traffic Simulation SUMO, Proc. 3rd Industrial Simulation Conf. 2005; Berlin, Germany, 2005.
  • [9] LV SH., et al.: Coordinate Signal Control in Urban Traffic of Two-direction Green Wave based on Genetic BP Neural Network, Proceedings of the 2012 International Conference on Automobile and Traffic Science, Materials, Metallurgy Engineering 2012.
  • [10] SANCHEZ J.J., GALAN M., RUBIO E.: Genetic algorithms and cellular automata: a new architecture for traffic light cycles optimization. Congress on Evolutionary Computation, CEC 2004, 2:1668 – 1674, 2004.
  • [11] TEKLU F., SUMALEE A., WATLING D.: A genetic algorithm approach for optimizing traffic control signals considering routing. Computer-Aided Civil and Infrastructure Engineering, 22:31 – 43, 2007.
  • [12] WARBERG A., LARSEN J.: Green Wave Traffic Optimization – A Survey, Informatics and Mathematical Modeling, IMM Technical Report-2008-01, 2008.
  • [13] LI X., TAN G., CHEN CH.: Urban arterial road green-wave control based on genetic algorithm, 7th World Congress on Intelligent Control and Automation, 2008.
  • [14] LIU X., YUE Y.: Study of Two Way Traffi c Green Wave Coordinated Control Optimization Method, Proceeding ICETCE ‚12 Proceedings of the 2012 Second International Conference on Electric Technology and Civil Engineering, 2012.
  • [15] MA CH., HE R.: Green wave traffic control system optimization based on adaptive genetic-artifi cial fish swarm algorithm, Theory And Applications Of Soft Computing Methods, Neural Computation and Applications, p. 1-11, 2015.
  • [16] Open Street Map homepage: https://www.openstreetmap.org [date of access: 20.02.2016].
  • [17] SRIVASTAVA S., DEB K.: A Genetic Algorithm Based Augmented Lagrangian Method for Computationally Fast Constrained Optimization, Computational Optimization and Applications, 53.3: s. 869-902, 2012.
  • [18] SUN D., BENEKOHAL R.F., S.T. WALLER, Multiobjective traffic signal timing optimization using non-dominated sorting genetic algorithm. IEEE IV 2003 Intelligent Vehicles Symposium. Proceedings, pp. 198-203, 2003.
  • [19] SZYMANSKI A., et al.: Two methods of calculation of the origination destination matrix of an urban area, Raport W04/P-007/15, Wrocław University of Technology, 2015.
  • [20] TREIBER M., KESTING A.: Traffic Flow Dynamics, Data, Models and Simulation, Springer Heidelberg, New York, 2013.
  • [21] WÜNSCH G.: Coordination of Traffic Signals in Networks. PhD thesis, Technische Universität Berlin, 2008.
  • [22] http://oldblog.antirez.com/post/picol.html [date of access: 20.02.2016].
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-51edb707-181c-4ab0-bd6e-cc769df18407
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