The Exploratory Advanced Research Program

A Primer for Agent-Based Simulation and Modeling in Transportation Applications

REFERENCES

1. Samuelson, D. A., & Macal, C. M. (2006). Agent-based simulation comes of age: Software opens up many new areas of application. OR/MS Today, 33(4).
2. Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. PANS, 99, 7280-7287.
3. Gilbert, N. (2007). Agent-based models. Thousand Oaks, CA: SAGE Publications, Inc.
4. Macal, C. M., & North, M. J. (2006b). Tutorial on agent-based modeling and simulation Part 2: How to model with agents. Proceedings of the 2006 Winter Simulation Conference, Monterey, CA.
5. Davidsson, P. (2002). Agent-based social simulation: A computer science view. Journal of Artificial Societies and Social Simulation, 5(1).
6. Niazi, M., & Hussain, A. (2011). Agent-based computing from multi-agent systems to agent-based models: A visual survey. Scientometrics, 89(2), 479-499.
7. Gardner, M. (1970). The fantastic combination of John Conway's new solitaire game "Life." Scientific American, 223, 120-123.
8. Macal, C. M., & North, M. J. (2009). Agent-based modeling and simulation. Proceedings of the 2009 Winter Simulation Conference, Austin, TX.
9. Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up: MIT Press.
10. Heath, B. L. (2010). The history, philosophy, and practice of agent-based modeling and the development of the conceptual model for simulation diagram. Unpublished doctoral thesis, Wright State University, Dayton, OH.
11. Macal, C. M., & North, M. J. (2006a). Tutorial on agent-based modeling and simulation Part 2: How to model with agents. Paper presented at the Winter Simulation Conference, Monterey, CA.
12. Samuelson, D. A. (2005). Agent of change: How agent-based modeling may transform social science. OR/MS Today, 32(1).
13. Jennings, N. R. (2000). On agent-based software engineering. Artificial Intelligence, 117(2), 277-296.
14. Hughes, H. P. N., Clegg, C. W., Robinson, M. A., & Crowder, R. M. (2012). Agent-based modelling and simulation: The potential contribution to organizational psychology. Journal of Occupational and Organizational Psychology, 85(3), 487-502.
15. Crowder, R. M., Robinson, M. A., Hughes, H. P. N., & Sim, Y.-W. (2012). The development of an agent-based modeling framework for simulating engineering team work. IEEE Transactions on systems, man, and cybernetics, Part A: Systems and humans, 42(6), 1425-1439.
16. Macal, C. M. (2003). Effects of global information availability in networks of supply chain agents. Paper presented at the Agent 2003 Conference on Challenges in Social Simulation, Chicago, IL.
17. Said, L. B., Bouron, T., & Drogoul, A. (2002). Agent-based interaction analysis of consumer behavior. Paper presented at the Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 1, Bologna, Italy.
18. Singer, H. M., Singer, I., & Herrmann, H. J. (2009). Agent-based model for friendship in social networks. Physical Review E80, 026113.
19. Klugi, F., Bazzan, A., & Ossowski, S. (Eds.). (2005). Applications of agent technology in traffic and transportation. Basel, Switzerland: Birkhäuser Verlag.
20. Aschwanden, G. D. P. A., Wullschleger, T., Muller, H., & Schmitt, G. (2012). Agent-based evaluation of dynamic city models: A combination of human decision processes and an emission model for transportation based on acceleration and instantaneous speed. Automation in Construction, 22, 81-89.
21. Caplat, P., Anand, M., & Bauch, B. (2008). Symmetric competition causes population oscillations in an individual-based model of forest dynamics. Ecological Modelling, 211(3-4), 491-500.
22. Perez, L., & Dragicevic, S. (2009). An agent-based approach for modeling dynamics of contagious disease spread. International Journal of Health Geographics, 8(50).
23.Heckbert, S. (2012). MayaSim: An agent-based model of the ancient Maya social-ecological system (Version 3). Retrived August 14, 2013, from http://hdl.handle.net/2286.0/oabm:3063
24. Bharathy, G. K., Yilmaz, L., & Tolk, A. (2012). Agent directed simulation for combat modeling and distributed simulation. In A. Tolk (Ed.), Engineering principles of combat modeling and distributed simulation, Hoboken, NJ: John Wiley & Sons, Inc.
25. Lee, S., Son, Y., & Jin, J. (2010). An integrated human decision making model for evacuation scenarios under a BDI framework. ACM Transactions on Modeling and Computer Simulation (TOMACS), 20(4), Article 23.
26. Gibson, F. P., Fichman, M., & Plaut, D. C. (1997). Learning in dynamic decision tasks: Computational model and empirical evidence. Organizational Behavior and Human Decision Processes, 71, 1-35.
27. Mosteller, F., & Nogee, P. (1951). An experimental measurement of utility. The Journal of Political Economy, 59, 371-404.
28. Opaluch, J. J., & Segerson, K. (1989). Rational roots of irrational behavior: New theories of economic decision-making. Northeastern Journal of Agricutural and Resource Economics, 18(2), 81-95.
29. Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69, 99-118.
30. Busemeyer, J. R., & Diederich, A. (2002). Survey of decision field theory. Mathematical Social Science, 43(3), 345-370.
31. Edwards, W. (1954). The theory of decision-making. Psychological Bulletin, 51(4), 380-417.
32. Einhorn, H. J. (1970). The use of nonlinear, noncompensatory models in decision making. Psychological Bulletin, 73, 221-230.
33. Payne, J. W. (1982). Contingent decision behavior. Psychological Bulletin, 92, 382-402.
34. Konar, A., & Charkraborty, U. K. (2005). Reasoning and unsupervised learning in a fuzzy cognitive map. Information Sciences, 170, 419-441.
35. Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). SOAR: An architecture for general intelligence. Artificial Intelligence, 33, 1-64.
36. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.
37. Rao, A. S., & Georgeff, M. P. (1998). Decision procedures for BDI logics. Journal of Logic and Computation, 8(3), 293-343.
38. Rothrock, L., & Yin, J. (2008). Integrating compensatory and noncompensatory decision making strategies in dynamic task environments. In T. Kugler, C. Smith, T. Connolly, & Y. Son (Eds.), Decision modeling and behavior in uncertain and complex environments (pp. 123-138). New York: Springer-Verlag.
39. Sirbiladez, G., & Gachechiladez, T. (2005). Restored fuzzy measures in expert decision-making. Information Sciences, 169, 71-95.
40. Zhao, X., & Son, Y. (2008). BDI-based human decision-making model in automated manufacturing systems. International Journal of Modeling and Simulation, 28(3), 347-356.
41. Norling, E. (2004). Folk psychology for human modeling: Extending the BDI paradigm. Paper presented at the Proceedings of International Conference on Autonomous Agents and Multi-Agent System, New York.
42. Kinny, D., Georgeff, M., & Rao, A. (1996). A methodology and modeling technique for systems of BDI agents. Paper presented at the Proceedings of the 7th European Workshop on Modeling Autonomous Agents in a Multi-Agent World, Eindhoven, The Netherlands.
43. Lee, S., & Son, Y.-J. (2009). Dynamic learning in human decision behavior under the extended belief-desire-intention framework. Paper presented at the Proceedings of the 2009 Industrial Engineering Research Conference, Miami, FL.
44. Buntine, W. (1991). Theory refinement on bayesian networks. Paper presented at the 7th Conference on Uncertainty in Artificial Intelligence, Los Angeles, CA.
45. Heckerman, D., Geiger, D., & Chickering, D. (1994). Learning bayesian networks: the combination of knowledge and statistical data. Paper presented at the Proceedings of 10th Conference on Uncertainty in Artificial Intelligence, Seattle, WA.
46. Lam, W., & Bacchus, F. (1993). Using causal information and local measures to learn bayesian networks. Paper presented at the Proceedings of the 9th Conference on Uncertainty in Artifical Intelligence, Washington, DC.
47. Suzuki, J. (1993). A construction of bayesian networks with discrete variables from data. Paper presented at the Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence, Washington, DC.
48. Pearl, J., & Verma, T. S. (1991). A theory of inferred causation. Paper presented at the 2nd International Conference on Principles of Knowledge Representation and Reasoning. San Francisco, CA: Morgan Kaufmann.
49. Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search. New York: Springer-Verlag.
50. Niculescu, R. S., Mitchell, T. M., & Rao, R. B. (2006). Bayesian network learning with parameter constraints. Journal of Machine Learning Research, 7, 1357-1383.
51.Djan-Sampson, P. O., & Sahin, F. (2004). Structural learning of bayesian networks from complete data using the scatter search documents. Paper presented at the IEEE International Conference on System, Man, and Cybernetics, The Hague, The Netherlands.
52.Fu, W., & Anderson, J. R. (2006). From recurrent choice to skill learning: A reinforcement-learning model. Journal of Experimental Psychology: General, 135(2), 184-206.
53. Gray, W. D., Sims, C. R., Fu, W., & Schoelles, M. J. (2006). The soft contraints hypothesis: A rational analysis approach to resource allocation for interactive behavior. Psychological Review, 113(3), 461-482.
54. Sun, R., Alusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychological Review, 112(1), 159-192.
55. Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, 25(2), 203-244.
56. Holroyd, C. B., & Coles, M. G. H. (2002). The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709.
57. Kim, S., Xi, H., Mungle, S., & Son, Y.-J. (2012). Modeling human interactions with learning under the extended belief-desire-intention framework. Paper presented at the Proceedings of the 2012 Industrial and Systems Engineering Research Conference, Orlando, FL.
58. Thomas, K. (1976). Conflict and conflict management. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 889-935). Chicago, IL: Rand McNally, Inc.
59. Elliott, E., & Kiel, L. D. (2002). Exploring cooperation and competition using agent-based modeling. National Academy of Sciences of the United States of America, 99(3), 7193-7194.
60. Danielson, P. (2002). Competition among cooperators: Altruism and reciprocity. National Academy of Sciences of the United States of America, 99(3), 7237-7242.
61. Macy, M. W., & Flache, A. (2002). Learning dynamics in social dilemmas. National Academy of Sciences of the United States of America, 99(3), 7229-7236.
62. Read, D. W. (2002). A multi-trajectory, competition model of emergent complexity in human social Organization. National Academy of Sciences of the United States of America, 99(10), 7251-7256.
63. Pan, X., Han, C. S., & Law, K. H. (2005). A multi-agent based simulation framework for the study of human and social behavior in egress analysis. Paper presented at the International Conference on Computing in Civil Engineering, Cancun, Mexico.
64. Zlotkin, G., & Rosenschein, J. S. (1991). Cooperation and conflict resolution via negotiation among autonomous agents in noncooperative domains. IEEE Transactions on Systems, Man, and Cybernetics, 21(6), 1317-1324.
65. Allan, R. (2010). Survey of agent based modelling and simulation tools. Final report. Warrington, UK: Computational Science and Engineering Department, STFC Daresbury Laboratory.
66. Lytinen, S. L., & Railsback, S. F. (2009). The evaluation of agent-based simulation platforms: A review of NetLogo 5.0 and ReLogo. Paper presented at the European Meetings on Cybernetics and Systems Research.
67. Nikolai, C., & Madey, G. (2009). Tools of the trade: A survey of various agent based modeling platforms. Journal of Aritificial Societies and Social Simulation, 12(No. 22).
68. Railsback, S. F., Lytinen, S. L., & Jackson, S. K. (2006). Agent-based simulation platforms: Review and development recommendations. Simulation, 82(9), 609-623.
69. Serenko, A., & Detlor, B. (2002). Agent toolkits: A general overview of the market and an assessment of instructor satisfaction with utlizing toolkits in the classroom. Research and Working Paper Series (Vol. No. 455). Ontario, Canada: Michael G. DeGroote School of Business, McMaster University.
70. Macal, C. M., & North, M. (2008). Agent-based modeling and simulation: ABMS examples. Paper presented at the Winter Simulation Conference, Miami, FL.
71. Tisue, S., & Wilensky, U. (2004). NetLogo: A simple environment for modeling complexity. Paper presented at the International Conference on Complex Systems, Boston, MA.
72. Wilensky, U. (2011). NetLogo 4.1.3 User Manual: Programming Guide.
73. NetLogo Web Site. (2013). Retrieved August 15, 2013, from http://ccl.northwestern.edu/netlogo/models/
74. MASON Web site. (2012). Retrieved August 15, 2013, from http://cs.gmu.edu/~eclab/projects/mason/
75. Luke, S. (2011). Multiagent simulation and the MASON library. Final report. Retrieved on August 15, 2013, from http://cs.gmu.edu/~sean/temp/mason.pdf
76. Repast Web Site. (2012). Retrieved August 15, 2013, from http://repast.sourceforge.net/repast_3/index.html.  
77. North, M. J., & Macal, C. M. (2011). Product design patterns for agent-based modeling. Paper presented at the Proceedings of the 2011 Winter Simulation Conference, Phoenix, AZ.
78. Ascape Web site. (2012). Retrived August 15, 2013, from http://ascape.sourceforge.net/index.html#Introduction.
79. AnyLogic Web site. (2013b). Overview. Retrieved August 15, 2013, from http://www.anylogic.com/overview.  
80. Lee, S., Son, Y., & Jin, J. (2008). Decision field theory extensions for behavior modeling in dynamic environment using bayesian belief network. Information Sciences, 178, 2297-2314.
81. Hicklin, J., Moler, C., Webb, P., Boisvert, R. F., Miller, B., Pozo, R., et al. (2008). JAMA: A Java Matrix Package. Retrieved August 15, 2013, from http://math.nist.gov/javanumerics/jama/
82. AnyLogic Web site. (2013a). Downloads. Retrieved August 15, 2013, from  http://www.anylogic.com/downloads.  
83. Nagel, K., Beckman, R. L., & Barrett, C. L. (1999). TRANSIMS for transportation planning. Paper presented at the 6th International Conference on Computers in  Urban Planning and Urban Management, Franco Angeli, Milano, Italy.
84. Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., & Nagel, K. (2009). MATSim-T: Architecture and simulation. In A. L. C. Bazzan & F. Klugl (Eds.), Multi-agent systems for traffic and transportation engineering (pp. 57-78). Hershey, PA: IGI Global.
85. Bradley, M., Bowman, J. L., & Griesenbeck, B. (2010). SACSIM: An applied activity-based model system with fine-level spatial and temporal resolution. Journal of Choice Modelling, 3(1), 5-31.
86. Goulias, K. G., Bhat, C. R., Pendyala, R. M., Chen, Y., Paleti, R., Londuri, K. C., et al. (2011). Simulator of activities, greenhouse emissions, networks, and travel (SimAGENT) in southern California. Paper presented at the Transportation Research Board 91st Annual Meeting, Washington, DC.
87. Pendyala, R. M., Kitamura, R., Kikuchi, A., Yomamoto, T., & Fujii, S. (2005). Florida activity mobility simulator. Transportation Research Record, 1921, 123-130.
88. Salvini, P. A., & Miller, E. (2005). ILUTE: An operational prototype of a comprehensive microsimulation model of urban systems. Networks and Spatial Economics, 5(2), 217-234.
89. Jayakrishnan, R., Mahmassani, H. S., & Hu, T. Y. (1994). An Evaluation Tool for Advanced Traffic Information and Management Systems in Urban Networks. Transportation research C(2C(3)), 129-147.
90. Mahmassani, H. S. (2000). DYNASMART-P programmer's and user's guides. Final report. Austin, TX.: Center for Transportation Research, The University of Texas at Austin.
91. Chiu, Y.-C., Nava, E., Zheng, H., and Bustillos, B. (2011). DynusT user's manual. Retrieved August 15, 2013, from http://dynust.net/wikibin/doku.php
92. Noh, H., Khani, A., Hickman, M., Tian, Y., Chiu, Y.-C., & Villalobos, J. (2012). Integrated vehicle and passenger simulation model considering a multimodal transportation network. Paper presented at the 4th Transportation Researcb Board Conference on Innovations in Travel Modeling (ITM), Tampa, FL
93. Pendyala, R., Konduri, K., Chiu, Y.-C., Hickman, M., Noh, H., Waddell, P., et al. (2012). Integrated land use-transport model system with dynamic time-dependent activity-travel microsimulation. Paper presented at the Transportation Research Board 91st Annual Meeting, Washington, DC.
94. INRO Web site. (2010). Retrieved August 15, 2013, from http://www.inro.ca/en/products/dynameq/index.php.
95. Kettner, S. (2006, October 8-12). Examining capacity increasing measures with the Dynameq model: equilibrium traffic assignment. Paper presented at the 13th World Congress and Exhibition on Intelligent Transportation Systems, London, UK.
96. Boyce, D., Zhang, Y.-F., & Lupa, M. R. (1994). Introducing feedback into the four-step travel forecasting procedure vs. the equilibrium solution of a combined model. Paper presented at the Transportation Research Board 73rd Annual Meeting, Washington, DC.
97.Rickert, M., & Nagel, K. (2001). Traffic simulation: Dynamic traffic assignment on parallel computers in TRANSIMS. Future Generation Computer Systems, 17(5), 637-648.
98. Nagel, K., Stretz, P., Pieck, M., Donnelly, R., & Barrett, C. L. (1998). TRANSIMS traffic flow characteristics. Final report. Washington, DC.: U.S. Department of Transportation.
99. Balmer, M., Cetin, N., Nagel, K., & Raney, B. (2004). Towards truly agent-based traffic and mobility simulations. Paper presented at the Third International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'04), New York, NY.
100. Charypar, D., & Nagel, K. (2005). Generating complete all-day activity plans with genetic algorithms. Transportation, 32(4), 369-397.
101. Cetin, N., Burri, A., & Nagel, K. (2003). A parallel queue model approach to traffic microsimulations. Paper presented at the Transportation Research Board 82nd Annual Meeting, Washington, DC.
102. Meister, K., Balmer, M., Axhausen, K. W., & Nagel, K. (2006). Planomat: A comprehensive scheduler for a large-scale multi-agent transportation simulation. Paper presented at the 6th Swiss Transport Research Conference, Monte Verita, Ascona, Switzerland.
103. Kitamura, R., Chen, C., & Pendyala, R. M. (1997). Generation of synthetic daily activity-travel patterns. Transportation Research Record, 1607, 154-162.
104. Hagerstrand, T. (1970). What about people in regional science? Papers of the Regional Science Association, 24, 7-21.
105. Bradley, M. A., Bowman, J. L., & Griesenbeck, B. (2007). Development and application of the SACSIM activity-based model system. Paper presented at the 11th World Conference on Transportation Research, Berkeley, CA.
106. Miller, E. J. (2011). Agent-Based Modeling for Integrated Transportation-Land Use Policy Analysis. Paper presented at Northwestern University, Evanston, IL.
107. Mohammadian, A., & Miller, E. J. (2003a). Dynamic Modeling of Household Automobile Transactions. Paper presented at the Transportation Research Board 82th Annual Meeting, Washington, DC.
108. Mohammadian, A., & Miller, E. J. (2003b). An empirical investigation of household vehicle type choice decision. Paper presented at the Transportation Research Board 82th Annual Meeting, Washington, DC.
109. Salvini, P., & Miller, E. J. (2003). ILUTE: An operational prototype of a comprehensive microsimulation model of urban systems. Paper presented at the 10th International Conference on Travel Behavior Research, Lucerne, Switzerland.
110. Miller, E. J., & Roorda, M. (2003). Prototype model of household activity-travel scheduling. Transportation Research Record, 1831, 114-121.
111. Miller, E. J., Farooq, B., Chingcuanco, F., & Wang, D. (2010). Microsimulating urban spatial dynamics: historical validation tests using the ILUTE model system. Paper presented at the Workshop on Urban Dynamics, Marbella, Chile.
112. Gao, W., Balmer, M., & Miller, E. J. (2010). Comparison between MATSim and EMME/2 on the grater Toronto and Hamilton area network. Transportation Research Record: Journal of the Transportation Research Board, 2197, 118-128.
113. Pendyala, R. M., Bhat, C. R., Goulias, K. G., Paleti, R., Konduri, K. C., Sidharthan, R., et al. (2012). Application of a socio-economic model system for activity-based modeling: experience from southern California. Paper presented at the Transportation Research Board 92nd Annual Meeting, Washington, DC.
114. Wang, F.-Y. (2005). Agent-based control for networked traffic management systems. IEEE Intelligent Systems, 20(5), 92-96.
115. NASA. (1985). The Space Station Program. NASA publication.
116.Dowell, M. L., & Bonnell, R. D. (1991). Learning for distributed artificial intelligence systems. Paper presented at the Twenty-Third Southeastern Symposium on System Theory, Columbia, SC.
117.Parunak, H. V. D. (1999). Industrial and practical applications of DAI. In G. Weiss (Ed.), Multiagent Systems. Cambridge, MA: MIT press.
118.Adler, J. L., & Blue, V. J. (2002). A cooperative multi-agent transportation management and route guidance system. Transportation Research Part C: Emerging Technologies, 10(5-6), 433-454.
119.Chen, B., Cheng, H. H., & Palen, J. (2012). Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems. Transportation Research Part C: Emerging Technologies, 17(1), 1-10.
120. Jennings, N. R., Sycara, K., & Wooldridge, M. J. (1998). A roadmap of agent research and development. International Journal of Autonomous Agents and Multi-Agent Systems, 1(1), 7-38.
121. Bernhardt, K. L. S. (2007).  Transportation Research Circular-Artificial intelligence in transportation: Information for application (No. E-C113). Washington, DC: Transportation Research Board of the National Academies.
122. Kikuchi, S., Rhee, J., & Teodorovic, D. (2002). Applicability of an agent-based modeling concept to modeling of transportation phenomena. Yugoslav Journal of Operations Research, 12(2).
123. Chen, B., & Cheng, H. H. (2010). A review of the applications of agent technology in traffic and transportation systems. IEEE Transactions on Intelligent Transportation Systems, 11(2), 485-497.
124. Parker, D. C., Manson, S. M., Janssen, M. A., Hoffimann, M. J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93(2), 314-337.
125. Burmeister, B., Doormann, J., & Matylis, G. (1997). Agent-oriented traffic simulation. Transactions of the Society for Computer Simulation International-special issue: Multi-agent systems and simulation, 14(2), 79-86.
126. Fischer, K., Chaib-draa, B., Muller, J. P., Pischel, M., & Gerber, C. (1999). A simulation approach based on negotiation and cooperation between agents: A case study. IEEE Transactions on Systems, Man, and Cybernetics, 29(4), 531-545.
127. Hidas, P. (2002). Modelling lane changing and merging in microscopic traffic simulation. Transportation Research Part C: Emerging Technologies, 10(5-6), 351-371.
128. Hidas, P. (2005). Modelling vehicle interactions in microscopic simulation of merging and weaving. Transportation Research Part C: Emerging Technologies, 13(1), 37-62.
129. Meignan, D., Simonin, O., & Koukam, A. (2007). Simulation and evaluation of urban bus-networks using a multiagent approach. Simulation Modelling Practice and Theory, 15(6), 659-671.
130. Bazzan, A. L. (2005). A distributed approach for coordination of traffic signal agents. Autonomous Agents and Multi-Agent Systems, 10(1), 131-164.
131. Botelho, L. M. (2000). A control structure for agent interaction. Paper presented at the Proceedings of the 2000 Intelligent Vehicles Conference, Dearborn, MI.
132. Choy, M. C., Cheu, R. L., Srinivasan, D., & Logi, F. (2003). Real-time coordinated signal control using agents with online reinforcement learning. Paper presented at the Transportation Research Board 82nd Annual Meeting, Washington, DC.
133. Choy, M. C., Srinivasan, D., & Cheu, R. L. (2003). Cooperative, hybrid agent architecture for real-time traffic signal control. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 33(5), 597-607.
134. Desjardins, C., Laumonier, J., & Chaib-draa, B. (2007). Urban traffic control based on learning agents. Paper presented at the Proceedings of IEEE Intelligent Transportation Systems Conference, Seattle, WA.
135. Katwijk, R. T. v., Koningsbruggen, P. v., Schutter, B. D., & Hellendoorn, J. (2005). Test bed for multiagent control systems in road traffic management. Transportation Research Record, 1910, 108-115.
136. Kosonen, L. (2003). Multi-agent fuzzy signal control based on real-time simulation. Transportation Research Part C: Emerging Technologies, 11(5), 389-403.
137. Roozemond, D. A. (1999). Using intelligent agents for urban traffic control systems. Paper presented at the Proceedings of the 6th World Congress on Intelligent Transport Systems, Toronto, Ontario, Canada.
138. Roozemond, D. A. (2001). Using intelligent agents for pro-active, real-time urban intersection control. European Journal of Operational Research, 131(2), 293-301.
139. Srinivasan, D., Choy, M. C., & Cheu, R. L. (2006). Neural networks for real-time traffic signal control. IEEE Transactions on Intelligent Transportation Systems, 7(3), 261-272.
140. Adler, J. L., Satapathy, G., Manikonda, V., Bowles, B., & Blue, V. J. (2005). A multi-agent approach to cooperative traffic management and route guidance. Transportation Research Part B: Methodological, 39(4), 297-318.
141. Balbo, F., & Pinson, S. (2005). Dynamic modeling of a disturbance in a multi-agent system for traffic regulation. Decision Support Systems, 41(1), 131-146.
142. Hernandez, J. Z., Ossowski, S., & Garcia-Serrano, A. (2002). Multiagent architectures for intelligent traffic management systems. Transportation Research Part C: Emerging Technologies, 10(5-6), 473-506.
143. Katwijk, R. v., & Koningsbruggen, P. v. (2002). Coordination of traffic management instruments using agent technology. Transportation Research Part C: Emerging Technologies, 10(5-6), 455-471.
144. Ossowski, S., Hernandez, J. Z., Belmonte, M.-V., Fernandez, A., Garcia-Serrano, A., Perez-de-la-Cruz, J. L., et al. (2005). Decision support for traffic management based on organisational and communicative multiagent abstractions. Transportation Research Part C: Emerging Technologies, 13(4), 272-298.
145. Dia, H. (2002). An agent-based approach to modelling driver route choice behavior under the influence of real-time information. Transportation Research Part C: Emerging Technologies, 10(5), 331-349.
146. Wahle, J., Bazzan, A. L. C., Klugl, F., & Schreckenberg, M. (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), 399-417.
147. Weyns, D., Holvoet, T., & Helleboogh, a. (2007). Anticipatory vehicle routing using delegate multi-agent systems. Paper presented at the Proceedings of the 2007 IEEE Intelligent Transportation Systems Conference, Seattle, WA.
148. Logi, F., & Ritchie, S. G. (2002). A multi-agent architecture for cooperative inter-jurisdictional traffic congestion management. Transportation Research Part C: Emerging Technologies, 10(5-6), 507-527.
149. Tomas, V. R., & Garcia, L. A. (2005). Agent-based management of non urban road meteorological incidents. Multi-Agent Systems and Applications IV. Lecture Notes in Computer Science, 3690, 213-222.
150. Belmonte, M.-V., Perez-de-la-Cruz, J. L., Triguero, F., & Fernandez, A. (2005). Agent coordination for bus fleet management. 2005 ACM Symposium on Applied Computing, 462-466.
151. Belmonte, M. V., Perez-de-la-Cruz, J. L., & Triguero, F. (2008). Ontologies and agents for a bus fleet management system. Expert Systems with Applications, 34(2), 1351-1365.
152. Blum, J., & Eskandarian, A. (2002). Enhancing intelligent agent collaboration for flow optimization of railroad traffic. Transportation Research Part A: Policy and Practice, 36(10), 919-930.
153. Bocker, J., Lind, J., & Zirkler, B. (2001). Using a multi-agent approach to optimise the train coupling and sharing system. European Journal of Operational Research, 131(2), 242-252.
154. Lind, J., & Fischer, K. (1999). Transportation scheduling and simulation in a railroad scenario: A multi-agent approach. Paper presented at the Proceedings of the Fourth International Conference on the Practical Application of Intelligent Agents and Multi-Agents, London, UK.
155. Tsang, C. W., & Ho, T. K. (2008). Optimal track access rights allocation for agent negotiation in an open railway market. IEEE Transactions on Intelligent Transportation Systems, 9(1), 68-82.
156. Lordanova, B. N. (2003). Air traffic knowledge management policy. European Journal of Operational Research, 146(1), 83-100.
157. Nitschke, G. (2010). Cooperating air traffic control agents. Applied Artificial Intelligence: An International Journal, 15(2), 209-235.
158. Wangermann, J. P., & Stengel, R. F. (1998). Principled negotiation between intelligent agents:A model for air traffic management. Artificial Intelligence in Engineering, 12, 177-187.
159. Scemama, G. (1994). CLAIRE: A context-free AI-based supervisor for traffic control. In M. Bielli, G. Ambrosino, & M. Boero (Eds.), Artificial intelligence applications to traffic engineering (pp. 137-156). Utrecht, The Netherlands: VSP.
160. Garcia-Serrano, A. M., Vioque, D. T., Carbone, F., & Mendez, V. D. (2003). FIPA-compliant MAS development for road traffic management with a knowledge-based approach: The TRACK-R agents. Paper presented at the Challenges in Open Agent System Workshop, Melbourne, Australia.
161. Bazzan, A. L. C., Wahle, J., & Klugl, F. (1999). Agents in traffic modelling-from reactive to social behavior. In G. Weiss (Ed.), Distributed artificial intelligence meets machine learning: New York: Springer-Verlag.
162. Wang, F.-Y. (2008). Toward a revolution in transportations: AI for complex systems. IEEE Intelligent Systems, 23(6), 8-13.
163. Wardrop, J. G. (1952). Some theoretic aspects of road traffic research. Paper presented at the Conference of the Institute of Civil Engineering II.
164. Mahmassani, H. S. (1987). On boundedly rational user equilibrium in transportation systems. Transportation Science, 21(2), 89-99.
165. Daganzo, C. F. (1982). Unconstrained extremal formulation of some transportation equilibrium problems. Transportation Science, 16(3), 342-344.
166. Peeta, S., & Ziliaskopoulos, A. K. (2001). Foundations of dynamic traffic assignment: The past, the present and the future. Network and Spatial Economics, 1, 233-265.
167. Chiu, Y.-C., Bottom, J., Mahut, M., Paz, A., Balakrishna, R., Waller, T., et al. (2011). Transportation Research Circular-Dynamic traffic assignment (No. E-C153). Washington, DC: Transportation Research Board of the National Academies.
168. Yang, Q., & Koutsopoulos, H. N. (1996). A microsopic traffic simulator for evaluation of dynamic traffic management systems. Transportation Research Part C: Emerging Technologies, 4(3), 113-129.
169. Mahmassani, H. S., & Hawas, Y. E. (1998). A hierarchical distributed computational architecture for a centralized real-time dynamic traffic assignment. Paper presented at the 3rd TRIannual Symposium on Transportation ANalysis (TRISTAN III), San Juan, Puerto Rico.
170. Nakayama, S. (2009). Bayesian learning, day-to-day adjustment process, and stability of Wardrop equilibrium. Paper presented at the 18th International Symposium on Transportation and Traffic Theory, Hong Kong, China.
171. Ben-Akiva, M., & Lerman, S. R. (1985). Discrete choice analysis: Theory and application to travel demand. Cambridge, MA: MIT Press.
172. Sheffi, Y. (1984). Urban transportation networks: Equilibrium analysis with mathematical programming methods. Englewood Cliffs, NJ: Prentice-Hall, Inc.
173. Newell, G. F. (2002). A simplified car-following theory: A lower order model. Transportation Research Part B: Methodological, 36(3), 195-205.
174. Yeo, H., Skarbodonis, A, Halkias, J., Colyar, J., & Alexiadis, V. (2008). Oversaturated freeway flow algorithm for use in next generation simulation. Transportation Research Record, 2088, 68-79.
175. Laval, J. A., & Daganzo, C. F. (2006). Lane-changing in traffic streams. Transportation Research Part B: Methodological, 40(3), 251-264.
176. Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics, 106(4), 1039-1061.