Skip to main content
Log in

Optimizing urban traffic control using a rational agent

  • Published:
Journal of Zhejiang University SCIENCE C Aims and scope Submit manuscript

Abstract

This paper is devoted to developing and evaluating a set of technologies with the objective of designing a methodology for the implementation of sophisticated traffic lights by means of rational agents. These devices would be capable of optimizing the behavior of a junction with multiple traffic signals, reaching a higher level of autonomy without losing reliability, accuracy, or efficiency in the offered services. In particular, each rational agent in a traffic signal will be able to analyze the requirements and constraints of the road, in order to know its level of demand. With such information, the rational agent will adapt its light cycles with the view of accomplishing more fluid traffic patterns and minimizing the pollutant environmental emissions produced by vehicles while they are stopped at a red light, through using a case-based reasoning (CBR) adaptation. This paper also integrates a microscopic simulator developed to run a set of tests in order to compare the presented methodology with traditional traffic control methods. Two study cases are shown to demonstrate the efficiency of the introduced approach, increasing vehicular mobility and reducing harmful activity for the environment. For instance, in the first scenario, taking into account the studied traffic volumes, our approach increases mobility by 23% and reduces emissions by 35%. When the roads are managed by sophisticated traffic lights, a better level of service and considerable environmental benefits are achieved, demonstrating the utility of the presented approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Abramson, M., Chao, W., Macker, J., et al., 2008. Coordination in disaster management and response: a unified approach. LNAI, 5043:162–175. [doi:10.1007/978-3-540-85449-4_12]

    Google Scholar 

  • Adhau, S., Mittal, M.L., Mittal, A., 2012. A multi-agent system for distributed multi-project scheduling: an auction-based negotiation approach. Eng. Appl. Artif. Intell., 25(8):1738–1751. [doi:10.1016/j.engappai.2011.12.003]

    Article  Google Scholar 

  • Balbo, F., Pinson, S., 2005. Dynamic modeling of a disturbance in a multi-agent system for traffic regulation. Dec. Support Syst., 41(1):131–146. [doi:10.1016/j.dss.2004.06.001]

    Article  Google Scholar 

  • Berkhin, P., 2006. A survey of clustering data mining techniques. In: Grouping Multidimensional Data—Recent Advances in Clustering. Springer Berlin Heidelberg, p.25–71. [doi:10.1007/3-540-28349-8_2]

    Chapter  Google Scholar 

  • Borne, P., Fayech, B., Hammadi, S., et al., 2003. Decision support system for urban transportation networks. IEEE Trans. Syst. Man Cybern. C, 33(1):67–77. [doi:10.1109/TSMCC.2003.809355]

    Article  Google Scholar 

  • Chan, F.T.S., Zhang, J., 2002. A multi-agent-based agile shop floor control system. Int. J. Adv. Manuf. Technol., 19(10): 764–774. [doi:10.1007/s001700200088]

    Article  Google Scholar 

  • Chen, B., Cheng, H., 2010. A review of the applications of agent technology in traffic and transportation systems. IEEE Trans. Intell. Transp. Syst., 11(2):485–497. [doi:10. 1109/TITS.2010.2048313]

    Article  Google Scholar 

  • Chen, B., Cheng, H.H., Palen, J., 2006. Mobile-C: a mobile agent platform for mobile C/C++ agents. J. Softw. Pract. Exp., 36:1711–1733. [doi:10.1002/spe.742]

    Article  Google Scholar 

  • Chen, R.S., Chen, D.K., Lin, S.Y., 2005. ACTAM: cooperative multiagent system architecture for urban traffic signal control. Proc. IEICE Trans. Inf. Syst., E88-D(1):119–126. [doi:10.1093/ietisy/E88-D.1.119]

    Article  Google Scholar 

  • Cupek, R., Maka, A., 2010. OPC UA for vertical communication in logistic informatics systems. 15th IEEE Int. Conf. on Emerging Technologies and Factory Automation. [doi:10.1109/ETFA.2010.5640978]

    Google Scholar 

  • D’Amours, S., Frayret, J.M., Rousseau, A., et al., 2007. Agent-based supply-chain planning in the forest products industry. Information Technology for Balanced Manufacturing Systems. IFIP International Federation for Information Processing, 220:17–26. [doi:10.1007/978-0-387-36594-7_3]

    Article  Google Scholar 

  • de Mantaras, L., Bridge, D., Mcsherry, D., 1997. Case-based reasoning: an overview. AI Commun., 10:21–29.

    Google Scholar 

  • di Lecce, V., Amato, A., Soldo, D., et al., 2010. A multi agent system modelling an intelligent transport systems. In: Cakaj, S. (Ed.), Modelling, Simulation and Optimizatio—Focus on Applications, p.135–146.

    Google Scholar 

  • Epstein, J.M., 2007. Generative Social Sciences: Studies in Agent-Based Computational Modeling. Princeton University Press, New Jersey, USA.

    Google Scholar 

  • Fard, F.H., Far, B.H., 2012. A method for detecting agents that will not cause emergent behavior in agent based systems: a case study in agent based auction systems. IEEE 13th Int. Conf. on Information Reuse and Integration, p.185–192. [doi:10.1109/IRI.2012.6303009]

    Google Scholar 

  • Finin, T., Weber, J., Wiederhold, G., et al., 1993. DRAFT Specification of the KQML Agent-Communication Language. Technical Report EIT TR 92-04, Enterprise Communication Technologies, Palo Alto, CA.

    Google Scholar 

  • Folcik, V.A., Broderick, G., Mohan, S., et al., 2011. Using an agent-based model to analyze the dynamic communication network of the immune response. Theor. Biol. Med. Model., 8(1):1. [doi:10.1186/1742-4682-8-1]

    Article  Google Scholar 

  • Garcia-Serrano, A.M., Teruel, D., Carbone, F., et al., 2003. FIPA-compliant MAS development for road traffic management with a knowledge-based approach: the TRACK-R agents. Proc. Challenges Open Agent System Workshop.

    Google Scholar 

  • Hernandez, J.Z., Ossowski, S., Garcia-Serrano, A., 2002. Multiagent architectures for intelligent traffic management systems. Transp. Res. Part C, 10(5–6):473–506. [doi:10.1016/S0968-090X(02)00032-3]

    Article  Google Scholar 

  • Hernandez Encinas, A., Hernandez Encinas, L., Hoya White, S., et al., 2007. Simulation of forest fire fronts using cellular automata. Adv. Eng. Softw., 38(6):372–378. [doi:10.1016/j.advengsoft.2006.09.002]

    Article  MATH  Google Scholar 

  • Huang, C.Y., Cheng, K., Holt, A., 2007. An integrated manufacturing network management framework by using mobile agent. Int. J. Adv. Manuf. Technol., 32(7–8):822–833. [doi:10.1007/s00170-005-0378-1]

    Article  Google Scholar 

  • Huang, S., Sadek, A., Zhao, Y., 2012. Assessing the mobility and environmental benefits of reservation-based intelligent intersections using an integrated simulator. IEEE Trans. Intell. Transp. Syst., 13(3):1201–1214. [doi:10. 1109/TITS.2012.2186442]

    Article  Google Scholar 

  • Jennings, N.R., Sycara, K., Woolridge, M., 1998. A roadmap of agent research and development. Auton. Agents Multiagent Syst., 1(1):7–38. [doi:10.1023/A:1010090405266]

    Article  Google Scholar 

  • Kaihara, T., 2008. A multiagent-based complex systems approach for dynamic negotiation mechanism in virtual enterprise. Robot. Comput.-Integr. Manuf., 24(5):656–663. [doi:10.1016/j.rcim.2007.09.006]

    Article  Google Scholar 

  • Liu, Z.Q., Ishida, T., Sheng, H.Y., 2005. Multiagent-based demand bus simulation for Shanghai. Proc. Massively Multi-agent System I, 3446:309–322. [doi:10.1007/115 12073_23]

    Article  Google Scholar 

  • Maka, A., Cupek, R., Wierzchanowski, M., et al., 2011. Agent-based modeling for warehouse logistics systems. Int. Conf. on Computer Modelling and Simulation, p.151–155. [doi:10.1109/UKSIM.2011.37]

    Google Scholar 

  • Malveau, R., Mowbray, T.J., 2001. Software Architect Bootcamp. Prentice-Hall, Englewood Cliffs.

    Google Scholar 

  • Montealegre, N., Rammig, F.J., 2012. Agent-based modeling and simulation of artificial immune systems. IEEE 15th Int. Symp. on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops, p.212–219. [doi:10.1109/ISORCW.2012.43]

    Chapter  Google Scholar 

  • Nestinger, S.S., Chen, B., Cheng, H.H., 2010. A mobile agent-based framework for flexible automation systems. IEEE/ASME Trans. Mechatron., 15(6):942–951. [doi:10.1109/TMECH.2009.2036169]

    Google Scholar 

  • Ngai, E., Riggins, F., 2008. RFID: technology, applications, and impact on business operations. Int. J. Prod. Econ., 112(2):507–509.

    Article  Google Scholar 

  • Ossowski, S., Hernandez, J.Z., Belmonte, M.V., et al., 2005. Decision support for traffic management based on organisational and communicative multi-agent abstractions. Transp. Res. Part C, 13(4):272–298. [doi:10.1016/j.trc.2005.07.005]

    Article  Google Scholar 

  • Pérez, J., Seco, F., Milanés, V., et al., 2010. An RFID-based intelligent vehicle speed controller using active traffic signals. Sensors, 10(6):5872–5887. [doi:10.3390/s100605872]

    Article  Google Scholar 

  • Regli, W.C., Mayk, I., Dugan, C.J., et al., 2009. Development and specification of a reference model for agent-based systems. IEEE Trans. Syst. Man Cybern. Part C, 39(5): 572–596.

    Article  Google Scholar 

  • Robu, V., Noot, H., Poutré, H.L., et al., 2011. A multi-agent platform for auction-based allocation of loads in transportation logistics. Expert Syst. Appl., 38(4):3483–3491. [doi:10.1016/j.eswa.2010.08.136]

    Article  Google Scholar 

  • Roozemond, D.A., 2001. Using intelligent agents for proactive, real-time urban intersection control. Eur. J. Oper. Res., 131(2):293–301. [doi:10.1016/S0377-2217(00)00129-6]

    Article  MATH  Google Scholar 

  • Saeed, Y., Khan, S., Ahmed, K., et al., 2011. A multi-agent based autonomous traffic lights control system using fuzzy control. Int. J. Sci. Eng. Res., 2(6):1–5.

    Google Scholar 

  • Scora, G., Barth, M., 2006. CMEM Version 3.01 User’s Guide. Available from http://www.cert.ucr.edu/cmem/.

    Google Scholar 

  • Singh, V.K., Gupta, A.K., 2009. Agent based models of social systems and collective intelligence. Int. Conf. on Intelligent Agent & Multi-agent Systems, p.1–7. [doi:10. 1109/IAMA.2009.5228085]

    Google Scholar 

  • Trappey, C.V., Trappey, A.J., Huang, C.J., et al., 2009. The design of a JADE-based autonomous workflow management system for collaborative SoC design. Expert Syst. Appl., 36(2):2659–2669. [doi:10.1016/j.eswa.2008.01.064]

    Article  Google Scholar 

  • van Katwijk, R.T., van Koningsbruggen, P., de Schutter, B., et al., 2005. Test bed for multiagent control systems in road traffic management. Transp. Res. Rec. J. Transp. Res. Board, 1910(1):108–115. [doi:10.3141/1910-13]

    Article  Google Scholar 

  • Wang, F.Y., 2005. Agent-based control for networked traffic management systems. IEEE Intell. Syst., 20(5):92–96. [doi:10.1109/MIS.2005.80]

    Article  Google Scholar 

  • Wang, F.Y., 2008. Toward a revolution in transportation operations: AI for complex systems. IEEE Intell. Syst., 23(6):8–13. [doi:10.1109/MIS.2008.112]

    Article  MATH  Google Scholar 

  • Wen, W., 2008. A dynamic and automatic traffic light control system for solving the road congestion problem. Expert Syst. Appl., 34(4):2370–2381. [doi:10.1016/j.eswa.2007.03.007]

    Article  Google Scholar 

  • Wu, D.J., 2001. Software agents for knowledge management: coordination in multi-agent supply chains and auctions. Expert Syst. Appl., 20(1):51–64. [doi:10.1016/S0957-4174(00)00048-8]

    Article  Google Scholar 

  • Zade, A.R., Dandekar, D.R., 2011. FPGA implementation of intelligent traffic signal controller based on neuro-fuzzy system. Int. Conf. on Advanced Computing, Communication and Networks, p.1310–1314.

    Google Scholar 

  • Zhang, G., Li, Y., 2010. Agent-based modeling and simulation for open complex systems. 2nd Int. Asia Conf. on Informatics in Control, Automation and Robotics, p.504–507. [doi:10.1109/CAR.2010.5456783]

    Google Scholar 

  • Zhang, H.S., Zhang, Y., Li, Z.H., et al., 2004. Spatialtemporal traffic data analysis based on global data management using MAS. IEEE Trans. Intell. Transp. Syst., 5(4):267–275. [doi:10.1109/TITS.2004.837816]

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Salvador Ibarra-Martínez.

Additional information

ORCID: Salvador IBARRA-MARTÍNEZ, http://orcid.org/0000-0002-7106-6010

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ibarra-Martínez, S., Castán-Rocha, J.A. & Laria-Menchaca, J. Optimizing urban traffic control using a rational agent. J. Zhejiang Univ. - Sci. C 15, 1123–1137 (2014). https://doi.org/10.1631/jzus.C1400037

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/jzus.C1400037

Key words

CLC number

Navigation