Hostname: page-component-f554764f5-44mx8 Total loading time: 0 Render date: 2025-04-13T06:00:34.543Z Has data issue: false hasContentIssue false
  • English
  • Français

A review on agent-based technology for traffic and transportation

Published online by Cambridge University Press:  03 May 2013

Ana L. C. Bazzan  and
Franziska Klügl
Ana L. C. Bazzan
Affiliation:
Instituto de Informática/PPGC, UFRGS, Caixa Postal 15064 91.501-970 Porto Alegre, RS, Brazil; e-mail: [email protected]
Franziska Klügl
Affiliation:
Örebro University, Fakultetsgatan 1, 70182 Örebro, Sweden; e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

In the last few years, the number of papers devoted to applications of agent-based technologies to traffic and transportation engineering has grown enormously. Thus, it seems to be the appropriate time to shed light over the achievements of the last decade, on the questions that have been successfully addressed, as well as on remaining challenging issues. In the present paper, we review the literature related to the areas of agent-based traffic modelling and simulation, and agent-based traffic control and management. Later we discuss and summarize the main achievements and the challenges.

Type
Articles
Information
The Knowledge Engineering Review , Volume 29 , Issue 3 , June 2014 , pp. 375 - 403
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Arentze, T., Timmermans, H. 2005. Representing mental maps and cognitive learning in micro-simulation models of activity-travel choice dynamics. Transportation 32, 321–340.Google Scholar
Arentze, T., Timmermans, H. 2008. Albatross: overview of the model, application and experiences. In Innovation in Travel Modeling 2008 Conference, Portland.Google Scholar
Au, T.-C., Shahidi, N., Stone, P. 2011. Enforcing liveness in autonomous traffic management. In Proceedings of the Twenty-Fifth Conference on Artificial Intelligence, Burgard, W. & Roth, D. (eds). The AAAI Press, 1317–1322.Google Scholar
Auld, J., Mohammadian, A. 2012. Activity planning process in the agent-based dynamic activity planning and travel scheduling (ADAPTS) model. Transportation Research Part A 46, 1386–1403.Google Scholar
Balan, G., Luke, S. 2006. History-based traffic control. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, Nakashima, H., Wellman, M. P., Weiss, G. & Stone, P. (eds). ACM Press, 616–621.Google Scholar
Balmer, M., Cetin, N., Nagel, K., Raney, B. 2004. Towards truly agent-based traffic and mobility simulations. In Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multi Agent Systems, AAMAS, Jennings, N., Sierra, C., Sonenberg, L. & Tambe, M. (eds). IEEE Computer Society, vol. 1, 60–67.Google Scholar
Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebre, N., Nagel, K. 2009. MATSim-T: architecture and simulation times. In Multi-Agent Systems for Traffic and Transportation Engineering, Bazzan A. L. & Klügl, F. (eds). IGI Global, 57–78.CrossRefGoogle Scholar
Barceló, J. (ed.) 2010. Fundamentals of Traffic Simulation. Springer.Google Scholar
Barceló, J., Codina, E., Casas, J., Ferber, J. L., García, D. 2004. Microscopic traffic simulation: a tool for the design, analysis and evaluation of intelligent transport systems. Journal of Intelligent and Robotic Systems 41, 173–203.CrossRefGoogle Scholar
Bazzan, A. L. C. 2005. A distributed approach for coordination of traffic signal agents. Autonomous Agents and Multiagent Systems 10(1), 131–164.CrossRefGoogle Scholar
Bazzan, A. L. C., de Oliveira, D., da Silva, B. C. 2010. Learning in groups of traffic signals. Engineering Applications of Artificial Intelligence 23, 560–568.CrossRefGoogle Scholar
Bazzan, A. L. C., de Oliveira, D., Klügl, F., Nagel, K. 2008. Adapt or not to adapt—consequences of adapting driver and traffic light agents. In Adaptive Agents and Multi-Agent Systems III, Tuyls, K., Nowe, A., Guessoum, Z. & Kudenko, D. (eds). Lecture Notes in Artificial Intelligence 4865, 1–14. Springer-Verlag.Google Scholar
Bazzan, A. L. C., do Amarante, M. B., da Costa, F. B. 2012. Management of demand and routing in autonomous personal transportation. Journal of Intelligent Transportation Systems 16(1), 1–11.Google Scholar
Bazzan, A. L. C., Junges, R. 2006. Congestion tolls as utility alignment between agent and system optimum. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, Nakashima, H., Wellman, M. P., Weiss, G. & Stone, P. (eds). ACM Press, 126–128.Google Scholar
Bazzan, A. L. C., Klügl, F. 2005. Case studies on the Braess paradox: simulating route recommendation and learning in abstract and microscopic models. Transportation Research C 13(4), 299–319.Google Scholar
Bazzan, A. L. C., Klügl, F. 2008. Re-routing agents in an abstract traffic scenario. In Advances in Artificial Intelligence, Zaverucha G. & da Costa, A. L. (eds). Lecture Notes in Artificial Intelligence 5249, 63–72. Springer-Verlag.Google Scholar
Bazzan, A. L. C., Wahle, J., Klügl, F. 1999. Agents in traffic modelling—from reactive to social behavior. In Advances in Artificial Intelligence, Extended version appeared in Proceedings of the UK Special Interest Group on Multi-Agent Systems (UKMAS), Lecture Notes in Artificial Intelligence 1701, 303–306. Springer.Google Scholar
Benenson, I., Martens, K. 2008. Geosimulation of parking in the city. In Proceedings of the 5th Workshop on Agents in Traffic and Transportation, at AAMAS 2008, May 13, Estoril, Portugal, Bazzan, A. L. C., Klügl, F. & Ossowski, S. (eds), 29–35.Google Scholar
Bhat, C. R., Koppelman, F. S. 2003. Activity-based modeling of travel demand. In Handbook of Transportation, Hall, R. (ed.). Springer, 39–65.Google Scholar
Burmeister, B., Doormann, J., Matylis, G. 1997. Agent-oriented traffic simulation. Transactions of the Society for Computer Simulation 14(2), 79–86.Google Scholar
Camponogara, E., Kraus, W. Jr. 2003. Distributed learning agents in urban traffic control. In The Portuguese Conference on AI, Moura-Pires, F. & Abreu, S. (eds). Lecture Notes in Computer Science 2902, 324–335. Springer.Google Scholar
Charypar, D., Nagel, K., Axhausen, K. W. 2007. An event-driven queue-based microsimulation of traffic flow. Transportation Research Record 2003, 35–40.Google Scholar
Chen, B., Cheng, H. H. 2010. A review of the applications of agent technology in traffic and transportation systems. IEEE Transactions in Intelligent Transportation Systems 11(2), 485–497.Google Scholar
Chmura, T., Pitz, T. 2007. An extended reinforcement algorithm for estimation of human behavior in congestion games. Journal of Artificial Societies and Social Simulation 10(2).Google Scholar
Davidsson, P., Henesey, L., Ramstedt, L., Törnquist, J., Wernstedt, F. 2005. An analysis of agent-based approaches to transport logistics. Transportation Research C 13, 255–271.CrossRefGoogle Scholar
de Oliveira, D., Ferreira, P. R. Jr, Bazzan, A. L. C. 2004. A swarm based approach for task allocation in dynamic agents organizations. In Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multi Agent Systems, AAMAS. IEEE Computer Society, vol. 3, 1252–1253.Google Scholar
de Oliveira, L. B., Camponogara, E. 2010. Multi-agent model predictive control of signaling split in urban traffic networks. Transportation Research Part C: Emerging Technologies 18(1), 120–139.Google Scholar
de Palma, A., Ben-Akiva, M., Brownstone, D., Holt, C., Magnac, T., McFadden, D., Moffatt, P., Picard, N., Train, K., Wakker, P., Walker, J. 2008. Risk, uncertainty and discrete choice models. Marketing Letters 19, 269–285.Google Scholar
Desjardins, C., Laumônier, J., Chaib-draa, B. 2009. Learning agents for collaborative driving. In Multi-Agent Systems for Traffic and Transportation, Bazzan, A. L. C. & Klügl, F. (eds). IGI Global, 240–260.Google Scholar
Di Taranto, M. 1989. UTOPIA. In Proceedings of the IFAC-IFIP-IFORS Conference on Control, Computers, Communication in Transportation. International Federation of Automatic Control, 245–252.Google Scholar
Dia, H. 2002. An agent-based approach to modeling driver route choice behaviour under the influence of real-time information. Transportation Research C 10(5–6), 331–349.Google Scholar
Diakaki, C., Papageorgiou, M., Aboudolas, K. 2002. A multivariable regulator approach to traffic-responsive network-wide signal control. Control Engineering Practice 10(2), 183–195.Google Scholar
do Amarante, M. d. B., Bazzan, A. L. C. 2012. Agent-based simulation of mobility in real-world transportation networks: effects of acquiring information and replanning en-route. In Proceedings of the 11th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-2012), Valencia, Spain, 1351–1352.Google Scholar
Doniec, A., Mandiau, R., Piechowiak, S., Espié, S. 2008. A behavioral multi-agent model for road traffic simulation. Engineering Applications of Artificial Intelligence 21(8), 1443–1454.Google Scholar
Dresner, K., Stone, P. 2004. Multiagent traffic management: a reservation-based intersection control mechanism. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, Jennings, N., Sierra, C., Sonenberg, L. & Tambe, M. (eds). IEEE Computer Society, 530–537.Google Scholar
Dresner, K., Stone, P. 2005. Multiagent traffic management: an improved intersection control mechanism. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, Dignum, F., Dignum, V., Koenig, S., Kraus, S., Singh, M. P. & Wooldridge, M. (eds). ACM Press, 471–477.Google Scholar
Dresner, K., Stone, P. 2006. Multiagent traffic management: Opportunities for multiagent learning. In LAMAS 2005, Tuyls, K., Hoen, P. J., Verbeeck, K. & Sen, S. (eds). Lecture Notes in Artificial Intelligence 3898, 129–138. Springer Verlag.Google Scholar
Dresner, K., Stone, P. 2008. A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research 31, 591–656.Google Scholar
Ehlert, P. A. M., Rothkrantz, L. J. M. 2001. A reactive driving agent for microscopic traffic simulation. In Proceedings of the 15th European Simulation Conference, Prag, 2001, Kerckhoffs, E. J. H. & Snorek, M. (eds). SCS Publishing House, 943–949.Google Scholar
Epstein, J. M. 2007. Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity). Princeton University Press.Google Scholar
Espié, S., Auberlet, J. M. 2007. ARCHISIM: a behavioural multi-actor traffic simulation model for the study of a traffic system including ITS aspects. International Journal of ITS Research 5, 7–16.Google Scholar
Ettema, D., Arentze, T., Timmermans, H. 2011. Social influences on household location, mobility and activity choice in integrated micro-simulation models. Transportation Research Part A 45, 283–295.Google Scholar
France, J., Ghorbani, A. A. 2003. A multiagent system for optimizing urban traffic. In Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology. IEEE Computer Society, 411–414.Google Scholar
Gartner, N. H. 1983. OPAC—a demand-responsive strategy for traffic signal control. Transportation Research Record 906, 75–81.Google Scholar
Gershenson, C. 2007. Design and Control of Self-organizing Systems. PhD thesis, Vrije Universiteit Brussel.Google Scholar
Gilbert, N. 2007. Agent-based Models (Quantitative Applications in the Social Sciences). Sage Publications.Google Scholar
Gipps, P. G. 1981. A behavioural car-following model for computer simulation. Transportation Research Part B 15, 105–111.CrossRefGoogle Scholar
Grether, D., Chen, Y., Rieser, M., Beuck, U., Nagel, K. 2008. Emergent effects in multi-agent simulations of road pricing. In 48th Congress of the European Regional Science Association, August 2008, Liverpool, UK.Google Scholar
Guestrin, C., Lagoudakis, M. G., Parr, R. 2002. Coordinated reinforcement learning. In Proceedings of the Nineteenth International Conference on Machine Learning (ICML), Sammut, C. & Hoffmann, A. G. (eds). Morgan Kaufmann, 227–234.Google Scholar
Hackney, J., Marchal, F. 2011. A coupled multi-agent microsimulation of social interactions and transportation behavior. Transportation Research A 45, 296–309.Google Scholar
Han, Q., Arentze, T., Timmermans, H., Janssens, D., Wets, G. 2009. A multi-agent modeling approach to simulate dynamic activity-travel patterns. In Multi-Agent Systems for Traffic and Transportation Engineering, Bazzan, A. L. & Klügl, F. (eds). IGI Global, 36–56.Google Scholar
Han, Q., Arentze, T., Timmermans, H., Janssens, D., Wets, G. 2011. The effects of social networks on choice set dynamics: results of numerical simulations using an agent-based approach. Transportation Research A 45, 310–322.Google Scholar
Helbing, D., Lämmer, S., Lebacque, P. 2005. Self-organized control of irregular or perturbed network traffic. In Optimal Control and Dynamic Games, Deissenberg, C. & Hartl, R. (eds). Springer, 239.CrossRefGoogle Scholar
Henry, J., Farges, J. L., Tuffal, J. 1983. The PRODYN real time traffic algorithm. In Proceedings of the International Federation of Automatic Control (IFAC) Conference, Isermann, R. (ed.). IFAC, 307–312.Google Scholar
Horni, A., Nagel, K., Axhausen, K. W. 2011. High-resolution destination choice in agent-based demand models. Working Paper 682, Institute for Transport Planning and Systems, ETH Zürich.Google Scholar
Horni, A., Scott, D. M., Balmer, M., Axhausen, K. W. 2009. Location choice modeling for shopping and leisure activities with matsim: combining micro-simulation and time geography. Transportation Research Record 2135, 87–95.Google Scholar
Hunt, P. B., Robertson, D. I., Bretherton, R. D., Winton, R. I. 1981. SCOOT—a traffic responsive method of coordinating signals. TRRL Lab. Report 1014, Transport and Road Research Laboratory.Google Scholar
Joubert, J. W., Fourie, P. J., Axhausen, K. W. 2010. Large-scale agent-based combined traffic simulation of private cars and commercial vehicles. Transportation Research Record 2168, 24–32.Google Scholar
Junges, R., Bazzan, A. L. C. 2008. Evaluating the performance of DCOP algorithms in a real world, dynamic problem. In Proceedings of the 7th International Joint Conference on Automatic Agents and Multiagent Systems, Padgham, L., Parkes, D., Müller, J. & Parsons, S. (eds). IFAAMAS, 599–606.Google Scholar
Kesting, A., Treiber, M., Helbing, D. 2009. Agents in traffic simulation. In Agents, Simulations and Applications, Uhrmacher, A. & Weyns, D. (eds). Taylor and Francis, 325–356.CrossRefGoogle Scholar
Klügl, F., Bazzan, A. L. C. 2004. Simulated route decision behaviour: simple heuristics and adaptation. In Human Behaviour and Traffic Networks, Selten, R. & Schreckenberg, M. (eds). Springer, 285–304.Google Scholar
Klügl, F., Bazzan, A. L. C. 2012. Agent-based Modeling and Simulation. AI Magazine.Google Scholar
Klügl, F., Bazzan, A. L. C., Wahle, J. 2003. Selection of information types based on personal utility—a testbed for traffic information markets. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Gini, M., Ishida, T., Castelfranchi, C. & Lewis Johnson, W. (eds). ACM Press, 377–384.Google Scholar
Klügl, F., Rindsfüser, G. 2011. Agent-based route (and mode) choice simulation in real-world networks. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Lyon, France, 22–29.Google Scholar
Kosonen, I. 2003. Multi-agent fuzzy signal control based on real-time simulation. Transportation Research C 11(5), 389–403.Google Scholar
Koźlak, J., Dobrowolski, G., Kisiel-Dorohinicki, M., Nawarecki, E. 2008. Anti-crisis management of city traffic using agent-based approach. Journal of Universal Computer Science 14(14), 2359–2380.Google Scholar
Ksontini, F., Espié, S., Guessoum, Z., Mandiau, R. 2012. Traffic behavioral simulation in urban and suburban—representation of the drivers’ environment. In Advances on Practical Applications of Agents and Multi-Agent Systems, Demazeau, Y., Müller, J. P., Rodríguez, J. M. C. & Pérez, J. B. (eds). Springer, 115–125.Google Scholar
Kuyer, L., Whiteson, S., Bakker, B., Vlassis, N. A. 2008. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Proceedings of the ECML/PKDD, Antwerp, Belgium, 656–671.Google Scholar
Lowrie, P. 1982. The Sydney coordinate adaptive traffic system—principles, methodology, algorithms. In Proceedings of the International Conference on Road Traffic Signalling, Sydney, Australia.Google Scholar
Luo, Y., Boloni, L. 2012. Modeling the conscious behavior of drivers for multi-lane highway driving. In Proceedings of the 7th Workshop on Agent in Traffic and Transportation, Valencia.Google Scholar
Machado, A. M., Bazzan, A. L. C. 2011. Self-adaptation in a network of social drivers: using random boolean networks. In Proceedings of the Workshop on Organic Computing (OC 11), Müller-Schloer, C., Schmeck, H. & Ungerer, T. (eds). ACM, 33–40.Google Scholar
Mandiau, R., Champion, A., Auberlet, J.-M., Espié, S., Kolski, C. 2008. Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation. Applied Intelligence 28(2), 121–138.Google Scholar
McBreen, J., Jensen, P., Marchal, F. 2006. An agent-based simulation model of traffic congestion. In Proceedings of the 4th Workshop on Agents in Traffic and Transportation, Bazzan, A. L. C., Chaib-Draa, B., Klügl, F. & Ossowski, S. (eds), 43–49.Google Scholar
Nagel, K., Schreckenberg, M. 1992. A cellular automaton model for freeway traffic. Journal de Physique I 2, 2221–2229.Google Scholar
Navarro, L., Flacher, F., Corruble, V. 2011. Dynamic level of detail for large scale agent-based urban simulations. In Proceedings of 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), Tumer, K., Yolum, P., Sonenberg, L. & Stone (eds). IFAAMAS, 701–708.Google Scholar
Nunes, L., Oliveira, E. C. 2004. Learning from multiple sources. In Proccedings of the 3rd International Joint Conference on Autonomous Agents and Multi Agent Systems, Jennings, N., Sierra, C., Sonenberg, L. & Tambe, M. (eds). IEEE Computer Society, vol. 3, 1106–1113.Google Scholar
de Oliveira, D., Bazzan, A. L. C. 2009. Multiagent learning on traffic lights control: effects of using shared information. In Multi-Agent Systems for Traffic and Transportation, Bazzan, A. L. C. & Klügl, F. (eds). IGI Global, 307–321.Google Scholar
de Oliveira, D., Bazzan, A. L. C., Lesser, V. 2005. Using cooperative mediation to coordinate traffic lights: a case study. In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS), Dignum, F., Dignum, V., Koenig, S., Kraus, S., Singh, M. P. & Wooldridge, M. (eds). IEEE Computer Society, 463–470.Google Scholar
Ortúzar, J., Willumsen, L. G. 2001. Modelling Transport, 3rd edition. John Wiley & Sons.Google Scholar
Panwei, S., Dia, H. 2006. A fuzzy neural approach to modeling behavioural rules in agent-based route choice simulations. In Proceedings of the 4th Workshop on Agents in Traffic and Transportation, AAMAS 2006, May 9, Bazzan, A. L. C., Chaib-Draa, B., Klügl, F. & Ossowski, S. (eds), 70–78.Google Scholar
Papageorgiou, M. 2003. Chapter 8: Traffic control. In Handbook of Transportation Science, Hall, R. W. (ed.). Kluwer Academic Publisher, 243–277.Google Scholar
Paruchuri, P., Pullalarevu, A. R., Karlapalem, K. 2002. Multi agent simulation of unorganized traffic. In Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). ACM Press, vol. 1, 176–183.Google Scholar
Prashanth, L., Bhatnagar, S. 2011. Reinforcement learning with function approximation for traffic signal control. IEEE Transaction on Intelligent Transportation Systems 12(2), 412–421.Google Scholar
Prothmann, H., Tomforde, S., Branke, J., Hähner, J., Müller-Schloer, C., Schmeck, H. 2011. Organic traffic control. In Organic Computing A Paradigm Shift for Complex Systems, Müller-Schloer, C., Schmeck, H. & Ungerer, T. (eds). Springer, 431–446.Google Scholar
Rindsfüser, G., Klügl, F., Freudenstein, J. 2004. Multi-agent simulation for the generation of individual activity programs. In Application of Agent Technology in Traffic and Transportation, Klügl, F., Bazzan, A. L. C. & Ossowski, S. (eds). Birkhäuser, 165–180.Google Scholar
Rindt, C. R., Marca, J. E., McNally, M. G. 2002. An agent-based activity microsimulator kernel using a negotiation metaphor. Technical Report, Institute of Transportation Studies, University of California.Google Scholar
Robertson 1969. TRANSYT: a traffic network study tool. Rep. LR 253, Road Res. Lab.Google Scholar
Ronald, N. A., Arentze, T. A., Timmermans, H. J. P. 2011. The effects of different interaction protocols in agent-based simulation of social activities. International Journal of Agent Technologies and Systems 3(2), 18–32.Google Scholar
Roozemond, D. A. 2001. Using intelligent agents for pro-active, real-time urban intersection control. European Journal of Operational Research 131(2), 293–301.Google Scholar
Rossetti, R. J. F., Bordini, R. H., Bazzan, A. L. C., Bampi, S., Liu, R., Van Vliet, D. 2002. Using BDI agents to improve driver modelling in a commuter scenario. Transportation Research Part C: Emerging Technologies 10(5–6), 47–72.Google Scholar
Rossetti, R. J. F., Ferreira, P. A. F., Braga, R. A. M., Oliveira, E. C. 2008. Towards an artificial traffic control system. In Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems (ITSC 2008), Beijing, China, 14–19.Google Scholar
Schepperle, H., Böhm, K. 2007. Agent-based traffic control using auctions. In Proceedings of the CIA, Klusch, M., Hindriks, K. V., Papazoglou, M. P. & Sterling, L. (eds). Springer, 119–133.Google Scholar
Schepperle, H., Böhm, K. 2009. Valuation-aware traffic control—the notion and the issues. In Multi-Agent Systems for Traffic and Transportation, Bazzan, A. L. C. & Klügl, F. (eds). IGI Global, 218–239.Google Scholar
Schmeck, H. 2005. Organic computing—a new vision for distributed embedded systems. In ISORC, Lee, S. (ed.). IEEE Computer Society, 201–203.Google Scholar
Silva, B. C. d., Basso, E. W., Bazzan, A. L. C., Engel, P. M. 2006. Dealing with non-stationary environments using context detection. In Proceedings of the 23rd International Conference on Machine Learning ICML, Cohen, W. W. & Moore, A. (eds). ACM Press, 217–224.Google Scholar
Steingröver, M., Schouten, R., Peelen, S., Nijhuis, E., Bakker, B. 2005. Reinforcement learning of traffic light controllers adapting to traffic congestion. In Proceedings of the Seventeenth Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2005), Verbeeck, K., Tuyls, K., Nowé, A., Manderick, B. & Kuijpers, B. (eds). Koninklijke Vlaamse Academie van Belie voor Wetenschappen en Kunsten, 216–223.Google Scholar
Strippgen, D., Nagel, K. 2009. Multi-agent traffic simulation with cuda. In International Conference on High Performance Computing & Simulation, HPCS ‘09, Leipzig.CrossRefGoogle Scholar
Sun, Z., Arentze, T., Timmermans, H. 2012. A heterogeneous latent class model of activity rescheduling, route choice and information acquisition decision under multiple uncertain events. Transportation Research Part C 25, 46–60.Google Scholar
Teodorovic, D. 2008. Swarm intelligence systems for transportation engineering: principles and applications. Transportation Research Part C: Emerging Technologies 16(6), 651–667.CrossRefGoogle Scholar
Timmermans, H. 2005, editor Progress in Activity-based Analysis. Elsevier.Google Scholar
Timóteo, I. J. P. M., Araújo, M. R., Rossetti, R. J. F., Oliveira, E. C. 2012. Using TraSMAPI for the assessment of multi-agent traffic management solutions. Progress in Artificial Intelligence 1, 157–164.Google Scholar
Tumer, K., Welch, Z. T., Agogino, A. 2008. Aligning social welfare and agent preferences to alleviate traffic congestion. Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems, Padgham, L., Parkes, D., Müller, J. & Parsons, S. (eds). IFAAMAS, 655–662.Google Scholar
Tumer, K., Welch, Z. T., Agogino, A. 2009. Traffic congestion management as a learning agent coordination problem. In Multi-Agent Systems for Traffic and Transportation, Bazzan, A. L. C. & Klügl, F. (eds). IGI Global, 261–279.Google Scholar
van Katwijk, R., van Koningsbruggen, P. 2002. Coordination of traffic management instruments using agent technology. Transportation Research Part C: Emerging Technologies 10(5–6), 455–471.CrossRefGoogle Scholar
van Katwijk, R., van Koningsbruggen, P., De Schutter, B., Hellendoorn, J. 2005. A test bed for multi-agent control systems in road traffic management. In Applications of Agent Technology in Traffic and Transportation Whitestein Series in Software Agent Technologies and Autonomic Computing,, Klügl, F., Bazzan, A. L. C. & Ossowski, S. (eds). Birkhäuser, 113–131.CrossRefGoogle Scholar
Vasirani, M., Ossowski, S. 2009. A market-based approach to reservation-based urban road traffic management. In Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Decker, K., Sichman, J., Sierra, C. & Castelfranchi, C. (eds). IFAAMAS, 617–624.Google Scholar
Vasirani, M., Ossowski, S. 2011. A computational market for distributed control of urban road traffic systems. IEEE Transactions on Intelligent Transportation Systems 12(2), 313–321.Google Scholar
Wahle, J., Bazzan, A. L. C., Klügl, F. 2002. The impact of real time information in a two route scenario using agent based simulation. Transportation Research Part C: Emerging Technologies 10(5–6), 73–91.Google Scholar
Waizman, G., Shoval, S., Benenson, I. 2012. Micro-simulation model for assessing the risk of car-pedestrian road accidents. In Proceedings of the 7th Workshop on Agent in Traffic and Transportation, Valencia.Google Scholar
Wang, F.-Y. 2008. Toward a revolution in transportation operations: AI for complex systems. IEEE Intelligent Systems 23(6), 8–13.Google Scholar
Wardrop, J. G. 1952. Some theoretical aspects of road traffic research. Proceedings of the Institute of Civil Engineers 2, 325–378.Google Scholar
Wiering, M. 2000. Multi-agent reinforcement learning for traffic light control. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford, CA, USA, 1151–1158.Google Scholar
Yamashita, T., Izumi, K., Kurumatani, K. 2004. Analysis of the effect of route information sharing on reduction of traffic congestion. In Application of Agent Technology in Traffic and Transportation, Klügl, F., Bazzan, A. L. C. & Ossowski, S. (eds). Birkhäuser, 99–112.Google Scholar
Yamashita, T., Kurumatani, K. 2009. New approach to smooth traffic flow with route information sharing. In Multi-Agent Systems for Traffic and Transportation, Bazzan, A. L. C. & Klügl, F. (eds). IGI Global, 291–306.Google Scholar
Zhang, C., Abdallah, S., Lesser, V. 2009. Integrating organizational control into multi-agent learning. In Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), ao Sichman, J. S., Decker, K. S., Sierra, C. & Castelfranchi, C. (eds), 757–764. Budapest, Hungary.Google Scholar
Zhang, L., Levinson, D. 2004. Agent-based approach to travel demand modeling. Journal of the Transportation Research Board 1898, 28–36.CrossRefGoogle Scholar
Zhu, S., Levinson, D., Zhang, L. 2008. Agent-based route choice with learning and exchange of information. In Transportation Research Board Annual Meeting 2008 Paper Nr.08-2152.Google Scholar