Abstract
The problem of reducing traffic congestion in a city has always been difficult to solve with monolithic control methods, which have both high costs and increased implementation complexity. This paper aims to minimize vehicle waiting time at stoplights by using a multi-agent system control technology. Moreover, the system is required to respond adequately to the presence of emergency intervention vehicles, allowing them quick and sure passage, but without significantly interrupting regular traffic. The solution designed in this paper allows for on demand synchronization of intersections, depending on the traffic context at any given time. In order to test this concept, an agent based simulation model has been developed, that offers real world traffic simulations on urban maps, and integrated complex road networks and traffic participant behaviour, with a possibility to measure the performance of the control system through parameters such as noise levels and emissions.
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References
de Boer, J.: An attempt at more accurate estimation of the number of ambulances needed at disasters in The Netherlands. Prehosp. Disaster Med. 11(02), 125–128 (1996)
Blackwell, T.H., Kaufman, J.S.: Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad. Emerg. Med. 9(4), 288–295 (2002)
Sladjana, A., Gordana, P., Ana, S.: Emergency response time after out-of-hospital cardiac arrest. Eur. J. Intern. Med. 22(4), 386–393 (2011)
Pons, P.T., Haukoos, J.S., Bludworth, W., Cribley, T., Pons, K.A., Markovchick, V.J.: Paramedic response time: does it affect patient survival? Acad. Emerg. Med. 12(7), 594–600 (2005)
Barrachina, J., Garrido, P., Fogue, M., Martinez, F.J., Cano, J.C., Calafate, C.T., Manzoni, P.: Reducing emergency services arrival time by using vehicular communications and evolution strategies. Expert Syst. Appl. 41(4), 1206–1217 (2014)
White, J.: Emergency Vehicle Priority. In: The Queensland Surveying and Spatial Conference, Brisbane, Australia (2012)
Fiosina, J., Fiosins, M.: Resampling based modelling of individual routing preferences in a distributed traffic network. Int. J. Artif. Intell.™ 12(1), 79–103 (2014)
de Oliveira, D., Bazzan, A.L.: Traffic lights control with adaptive group formation based on swarm intelligence. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 520–521. Springer, Heidelberg (2006)
Wilkie, D., Sewall, J., Lin, M.C.: Transforming GIS data into functional road models for large-scale traffic simulation. IEEE Trans. Visual Comput. Graphics 18(6), 890–901 (2012)
Laitakari, J., Pakkala, D.: Dynamic context monitoring service for adaptive and context-aware applications. In: Eighth International Workshop on Applications and Services in Wireless Networks, 2008 ASWN 2008, pp. 11–19, October 2008
Jin, X., Jie, L.: A study of multi-agent based model for urban intelligent transport systems. Int. J. Adv. Comput. Technol. 4(6) (2012)
Chen, B., Cheng, H.H.: A review of the applications of agent technology in traffic and transportation systems. IEEE Trans. Intell. Transp. Syst. 11(2), 485–497 (2010)
Wang, N., Chen, Y., Zhang, L.: Design of multi-agent-based distributed scheduling system for bus rapid transit. In: 2011 International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 111–114. IEEE (2011)
Patrascu, M., Dragoicea, M., Ion, A.: Emergent intelligence in agents: a scalable architecture for smart cities. In: 18th International Conference on System Theory, Control and Computing (ICSTCC) 2014, pp. 181–186. IEEE (2014)
Jade. http://jade.tilab.com/. Accessed 01 Mar 2015
Casas, J., Ferrer, J.L., Garcia, G., Casas, J., Perarnau, J., Torday, A.: Traffic simulation with AIMSUN. In: Barceló, J. (ed.) ANTS 2006. International Series in Operations Research & Management Science, vol. 145, pp. 173–232. Springer, New York (2010)
Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., Nagel, K., Axhausen, K.: MATSim-T: Architecture and simulation times. In: Multi-agent Systems for Traffic and Transportation Engineering, pp. 57–78 (2009)
Gomes, G., May, A., Horowitz, R.: Congested freeway microsimulation model using VISSIM. Transp. Res. Rec. J. Transp. Res. Board 1876, 71–81 (2004)
Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO–simulation of urban mobility. Int. J. Adv. Syst. Meas. 5(3&4), 128–138 (2012)
Bellavista, P., Caselli, F., Foschini, L.: Implementing and evaluating V2X protocols over iTETRIS: traffic estimation in the COLOMBO project. In: Proceedings of the Fourth ACM International Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, pp. 25–32. ACM (2014)
RodrÃguez, T., Urquiza, A., Klunder, G.A.: The Amitran project contribution to the validation of methodologies for assessment of Intelligent Transport Systems. In: Transport Research Arena (TRA) 5th Conference: Transport Solutions from Research to Deployment (2014)
Katsaros, K., Kernchen, R., Dianati, M., Rieck, D.: Performance study of a Green Light Optimized Speed Advisory (GLOSA) application using an integrated cooperative ITS simulation platform. In: 2011 7th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 918–923. IEEE (2011)
JOSM Project. https://josm.openstreetmap.de. Accessed 01 Nov 2015
eWorld Project. http://eworld.sourceforge.net. Accessed 01 Nov 2015
Goelzer, B., Hansen, C.H., Sehrndt, G.: Occupational Exposure to Noise: Evaluation, Prevention and Control. World Health Organisation, Geneva (2001)
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Ion, A., Berceanu, C., Patrascu, M. (2016). Applying Agent Based Simulation to the Design of Traffic Control Systems with Respect to Real-World Urban Complexity. In: Rovatsos, M., Vouros, G., Julian, V. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2015 2015. Lecture Notes in Computer Science(), vol 9571. Springer, Cham. https://doi.org/10.1007/978-3-319-33509-4_31
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DOI: https://doi.org/10.1007/978-3-319-33509-4_31
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