Abstract
This chapter discusses a systems design approach inspired from the autonomic nervous system for intelligent transportation system (ITS) applications. This is done not with reference to the employed computing system but with reference to the requirements of traffic engineering applications. It is argued that the design and development of autonomic traffic management systems must identify the control loop that needs to be endowed with autonomic properties and subsequently use this framework for defining a desired set of self-∗ properties. A macroscopic network modelling application is considered for showing how autonomic system design can be used for defining and obtaining self-∗ properties, with particular emphasis given on self-optimisation. The interpretation of policies followed by network operators regarding route guidance is also discussed from the perspective of autonomic ITS.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kephart, J., Chess, D.: The vision of autonomic computing. IEEE Comput. 36(1), 41–50 (2003)
Warnier, M., van Sinderen, M., Brazier, M.: Adaptive knowledge representation for a self-managing home energy usage system. In: Proceedings of the Fourth International Workshop on Enterprise Systems and Technology (I-WEST), Athens, pp. 132–141 (2010)
Sterritt, R.: Autonomic networks: engineering the self-healing property. Eng. Appl. Artif. Intell. 17, 727–739 (2004)
Cheliotis, G., Kenyon, C.: Autonomic economics. In: Proceedings of the IEEE International Conference on E-Commerce (2003)
Truszkowski, W., Hallock, H., Rouff, C., Karlin, J., Rash, J., Sterritt, R.: Autonomous and Autonomic Systems: With Applications to NASA Intelligent Spacecraft Operations and Exploration Systems. NASA Monographs in Systems and Software Engineering. Springer, New York (2009)
Richter, U., Mnif, M., Branke, J., Müller-Schloer, C., Schmeck, H.: Towards a generic observer/controller architecture for organic computing. In: GI Jahrestagung (1)’06, pp. 112–119 (2006)
Etemadnia, H., Abdelghany, K., Hariri, S.: Toward an autonomic architecture for real-time traffic network management. J. Intell. Transp. Syst. Technol. Plan. Oper. 16, 45–59 (2012)
Dusparic, I., Cahill, V.: Multi-policy optimization in decentralized autonomic systems. In: Proceedings of 8th International Conference on Autonomous Agents and Multiagent Systems (2009)
Diao, Y., Hellerstein, J., Parekh, S., Griffith, R., Kaiser, G., Phung, D.: A control theory foundation for self-managing computing systems. IEEE J. Sel. Areas Commun. 23(12), 2213–2222 (2005)
Shaw, M.: “Self-Healing”: softening precision to avoid brittleness. In: Proceedings of ACM SIGSOFT WOSS ’02, pp. 111–114 (2002)
Kotsialos, A., Papageorgiou, M., Diakaki, C., Pavlis, Y., Middelham, F.: Traffic flow modeling of large-scale motorway networks using the macroscopic modelling tool METANET. IEEE Trans. Intell. Transp. Syst. 3, 282–292 (2002)
Guzman, C., Alcazar, V., Prior, D., Onaindia, E., Borrajo, D., Fdez-Olivarez, J., Quintero, E.: PELEA: a domain independent architecture for planning, execution and learning. In: Proceedings of ICAPS’12, pp. 38–45 (2012)
Poole, A., Kotsialos, A.: METANET model validation using a genetic algorithm. In: 13th IFAC Symposium on Control in Transportation Systems, pp. 7–12 (2012)
Munoz, L., Sun, X., Sun, D., Gomes, G., Horowitz, R.: Methodological calibration of the cell transmission model. In: Proceeding of the 2004 American Control Conference, Boston, pp. 798–803 (2004)
Munoz, L., Sun, X., Horowitz, R., Alvarez, L.: A piecewise-linearized cell transmission model and parameter calibration methodology. In: Proceeding of the 85th Transportation Research Board (TRB) Annual Meeting, Washington, DC, pp. 183–191 (2006)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence, IEEE, pp. 69–73 (1998)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the World on Congress on Computational Intelligence, vol. 2, pp. 1671–1676. IEEE, New York (2002)
Zhan, Z., Zhang, J., Li, Y., Chung, H.: Adaptive particle swarm optimization. IEEE Trans Syst. Man Cybern. B Cybern. 39(6), 1362–1381 (2009)
Zhang, Z., Jiang, Y., Zhang, S., Geng, S., Wang, H., Sang, G.: An adaptive particle swarm optimization algorithm for reservoir operation optimization. Appl. Soft Comput. 18, 167–177 (2014)
Gandomi, A.H., Yun, G.J., Yang, X.S., Talatahari, S.: Chaos-enhanced accelerated particle swarm optimization. Commun. Nonlinear Sci. Numer. Simul. 18(2), 327–340 (2013)
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, pp. 210–214. IEEE, New York (2009)
Walton, S., Hassan, O., Morgan, K., Brown, M.: Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44(9), 710–718 (2011)
Pavlis, Y., Papageorgiou, M.: Simple decentralized feedback strategies for route guidance in traffic networks. Transp. Sci. 33, 264–278 (1999)
Messmer, A., Papageorgiou, M.: Route diversion control in motorway networks via nonlinear optimization. IEEE Trans. Control Syst. Technol. 3, 144–154 (1995)
Kotsialos, A., Papageorgiou, M., Mangeas, M., Haj-Salem, H.: Coordinated and integrated control of motorway networks via non-linear optimal control. Transp. Res. C 10, 65–84 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Kotsialos, A., Poole, A. (2016). Autonomic Systems Design for ITS Applications: Modelling and Route Guidance. In: McCluskey, T., Kotsialos, A., Müller, J., Klügl, F., Rana, O., Schumann, R. (eds) Autonomic Road Transport Support Systems. Autonomic Systems. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-25808-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-25808-9_8
Published:
Publisher Name: Birkhäuser, Cham
Print ISBN: 978-3-319-25806-5
Online ISBN: 978-3-319-25808-9
eBook Packages: Computer ScienceComputer Science (R0)