Abstract
With the generalization of real-time traveler information, the behavior of modern transport networks becomes harder to analyze and to predict. It is now critical to develop simulation tools for mobility policies makers, taking into account this new information environment. Information is now individualized, and the interaction of a huge population of individually guided travelers have to be taken into account in the simulations. However, existing mobility multiagent and micro-simulations can only consider a sample of the real volumes of travelers, especially for big regions. With distributed simulations, it would be easier to analyze and predict the status of nowadays and future networks, with informed and connected travelers. In this paper, we propose a comparison between two methods for distributing multiagent travelers mobility simulations, allowing for the consideration of realistic travelers flows and wide geographical regions.
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Notes
- 1.
Coding the number of agents arriving or leaving an edge.
- 2.
The computational times are not strictly growing with the number of agents for the environment-based method. This is more likely due to the random origins and destinations of the agents. Therefore the simulation could sometimes be more complex with fewer agents.
References
Badeig, F., Balbo, F., Scemama, G., Zargayouna, M.: Agent-based coordination model for designing transportation applications. In: 11th International IEEE Conference on Intelligent Transportation Systems, 2008 (ITSC 2008), pp. 402–407, IEEE, 2008
Maciejewski, M., Nagel, K.: Towards multi-agent simulation of the dynamic vehicle routing problem in matsim. In: Proceedings of the 9th International Conference on Parallel Processing and Applied Mathematics—vol. Part II, PPAM’11, pp. 551–560. Springer, Berlin, Heidelberg (2012)
Nagel, K., Rickert, M.: Parallel implementation of the transims micro-simulation. Parallel Comput. 27(12), 1611–1639 (2001)
Chipeaux, S., Bouquet, F., Lang, C., Marilleau, N.: Modelling of complex systems with AML as realized in MIRO project. In: LAFLang 2011, Workshop of the International Conference WI/IAT (Web Intelligence and Intelligent Agent Technology), pp. 159–162. IEEE Computer Society, Lyon, France (2011)
Jakob, M., Moler, Z., Komenda, A., Yin, Z., Jiang, A.X., Johnson, M.P., Pechoucek, M., Tambe, M.: Agentpolis: towards a platform for fully agent-based modeling of multi-modal transportation (demonstration). In: International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012 (3 volumes), pp. 1501–1502, Valencia, Spain, 4–8 June 2012
Zargayouna, M., Zeddini, B., Scemama, G., Othman, A.: Simulating the impact of future internet on multimodal mobility. In: The 11th ACS/IEEE International Conference on Computer Systems and Applications AICCSA’2014. IEEE Computer Society (2014)
Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: SUMO - simulation of urban MObility - an overview. In: SIMUL 2011, The Third International Conference on Advances in System Simulation, pp. 55–60 (2011)
Collier, N., North, M.: Repast HPC: a platform for large-scale agent-based modeling. In: Dubitzky, W., Kurowski, K., Schott, B. (eds.) Large-Scale Computing, pp. 81–109. Wiley (2011)
Angelotti, E.S., Scalabrin, E.E., Avila, B.C.: PANDORA: a multi-agent system using paraconsistent logic. In: Proceedings of Fourth International Conference on Computational Intelligence and Multimedia Applications, 2001 (ICCIMA 2001), pp. 352–356 (2001)
Gulyas, L., Szemes, G., Kampis, G., de Back, W.: A modeler-friendly API for ABM partitioning. In: Proceedings of ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 219–226 (2009)
Mengistu, D., Lowis, MV.: An algorithm for optimistic distributed simulations. In: Modelling, Simulation, and Identification/658: Power and Energy Systems/660, 661, 662. ACTA Press (2011)
Scheutz, M., Schermerhorn, P.: Adaptive algorithms for the dynamic distribution and parallel execution of agent-based models. J. Parallel Distrib. Comput. 66(8), 1037–1051 (2006–2008)
Ng, B., Si, A., Lau, R.W.H., Li, F.W.B.: A multi-server architecture for distributed virtual walkthrough. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST ’02, pp. 163–170. ACM (2002)
Rihawi, O., Secq, Y., Mathieu, P.: Effective distribution of large scale situated agent-based simulations. In: ICAART 2014 6th International Conference on Agents and Artificial Intelligence, vol. 1, pp. 312–319. SCITEPRESS Digital Library (2014)
Bui, T.N., Jones, C.: Finding good approximate vertex and edge partitions is NP-hard. Inf. Process. Lett. 42(3), 153–159 (1992)
Walshaw, C.: Multilevel refinement for combinatorial optimisation: boosting metaheuristic performance. In: Hybrid Metaheuristics, number 114 in Studies in Computational Intelligence, pp. 261–289. Springer, Berlin (2008)
Fiduccia, C.M., Mattheyses, R.M.: A linear-time heuristic for improving network partitions. In: 19th Conference on Design Automation, 1982, pp. 175–181 (June 1982)
Langtangen, H.P., Cai, X.: On the efficiency of python for high-performance computing. In: Modeling. Simulation and Optimization of Complex Processes, pp. 337–357. Springer, Berlin (2008)
Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
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Mastio, M., Zargayouna, M., Rana, O. (2015). Towards a Distributed Multiagent Travel Simulation. In: Jezic, G., Howlett, R., Jain, L. (eds) Agent and Multi-Agent Systems: Technologies and Applications. Smart Innovation, Systems and Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-319-19728-9_2
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