Similar articles

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.

使用理性行为人优化城区交通控制

本文开发并评估了一套通过"理性行为人"实现先进交通信号灯的技术。这种交通信号灯可以优化具有多个交通信号灯路口的交通状况，从而提升自治性且不失可靠性、准确性和效率。特别的，面对交通信号，各个理性行为者可以分析对道路的需求以及限制，从而获知其需求水平。基于此信息，理性行为者通过实例推理（case-base dreasoning, CBR）调控其指示灯，以实现更大的交通流动性以及机动车在红灯停止状态下最少的环境污染物排放。本文采纳了一种微观仿真方法（microscopic simulator）用于所提方法与传统交通控制方法的比较。通过两个研究案例，本文方法在提升机动车流动性和减小对环境的损坏两方面体现了其有效性。例如，第一个案例中，考虑交通流量，本文方法可提升23%的流动性，降低35%的污染排放。以此先进交通灯控制道路，可提供更高服务水平和可观环境效益。
理性行为者；交通灯控制；优化；交通流动性

[1]Abramson, M., Chao, W., Macker, J., et al., 2008. Coordination in disaster management and response: a unified approach. LNAI, 5043:162-175.

[2]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.

[3]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.

[4]Berkhin, P., 2006. A survey of clustering data mining techniques. In: Grouping Multidimensional Data—Recent Advances in Clustering. Springer Berlin Heidelberg, p.25-71.

[5]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.

[6]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.

[7]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.

[8]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.

[9]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.

[10]Cupek, R., Maka, A., 2010. OPC UA for vertical communication in logistic informatics systems. 15th IEEE Int. Conf. on Emerging Technologies and Factory Automation.

[11]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.

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

[13]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.

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

[15]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.

[16]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.

[17]Folcik, V.A., Broderick, G., Mohan, S., et al., 2011. Using an agent-based model to analyze the dynamic communi-cation network of the immune response. Theor. Biol. Med. Model., 8(1):1.

[18]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.

[19]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.

[20]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.

[21]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.

[22]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.

[23]Jennings, N.R., Sycara, K., Woolridge, M., 1998. A roadmap of agent research and development. Auton. Agents Multi-agent Syst., 1(1):7-38.

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

[25]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.

[26]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.

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

[28]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.

[29]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.

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

[31]Ossowski, S., Hernandez, J.Z., Belmonte, M.V., et al., 2005. Decision support for traffic management based on organisational and communicative multiagent abstractions. Transp. Res. Part C, 13(4):272-298.

[32]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.

[33]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.

[34]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.

[35]Roozemond, D.A., 2001. Using intelligent agents for pro-active, real-time urban intersection control. Eur. J. Oper. Res., 131(2):293-301.

[36]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.

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

[38]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.

[39]Trappey, C.V., Trappey, A.J., Huang, C.J., et al., 2009. The design of a JADE-based autonomous workflow manage-ment system for collaborative SoC design. Expert Syst. Appl., 36(2):2659-2669.

[40]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.

[41]Wang, F.Y., 2005. Agent-based control for networked traffic management systems. IEEE Intell. Syst., 20(5):92-96.

[42]Wang, F.Y., 2008. Toward a revolution in transportation operations: AI for complex systems. IEEE Intell. Syst., 23(6):8-13.

[43]Wen, W., 2008. A dynamic and automatic traffic light control system for solving the road congestion problem. Expert Syst. Appl., 34(4):2370-2381.

[44]Wu, D.J., 2001. Software agents for knowledge management: coordination in multi-agent supply chains and auctions. Expert Syst. Appl., 20(1):51-64.

[45]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.

[46]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.

[47]Zhang, H.S., Zhang, Y., Li, Z.H., et al., 2004. Spatial-temporal traffic data analysis based on global data management using MAS. IEEE Trans. Intell. Transp. Syst., 5(4):267-275.