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References
Alam SJ, Werth B (2008) Studying emergence of clusters in a bus passengers’ seating preference model. Transport Res C Emerg Technol 16(5):593–614
Bazzan AL (2005) A distributed approach for coordination of traffic signal agents. Auton Agent Multi-Agent Syst 10:131–164
Ben-Akiva M, Lerman SR (1985) Discrete choice analysis: theory and application to travel demand. MIT Press, Cambridge, MA
Boxill S, Yu L (2000) An evaluation of traffic simulation models for supporting ITS development (Technical report, SWUTC/00/167602-1). Center for transportation training and research. Texas Southern University, Houston
Chen X, Zhan FB (2008) Agent-based modeling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies. J Oper Res Soc 59(1):25–33
Chung E, Dumont A-G (2009) Transport simulation: beyond traditional approaches. EFPL Press, Lausanne
Davidsson P, Verhagen H (2013) Types of simulation. Chapter 3 in this volume
Davidsson P, Henesey L, Ramstedt L, Törnquist J, Wernstedt F (2005) An analysis of agent-based approaches to transport logistics. Transport Res C Emerg Technol 13(4):255–271
Davidsson P, Holmgren J, Persson JA, Ramstedt L (2008) Multi agent based simulation of transport chains. In: Padgham L, Parkes D, Müller J, Parsons S (eds) Proceedings of the 7th international conference on autonomous agents and multiagent systems (AAMAS 2008), 12–16 May2008, Estoril. International Foundation for Autonomous Agents and Multiagent Systems, Richland, pp 1153–1160
de Jong G, Ben-Akiva M (2007) A micro-simulation model of shipment size and transport chain choice. Transport Res B 41(9):950–965
Dia H (2002) An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transport Res C 10(5–6):331–349
Dia H, Panwai S (2007) Modelling drivers’ compliance and route choice behavior in response to travel information. Nonlinear Dynam 49:493–509
Ehlert PAM, Rothkrantz LJM (2001) Microscopic traffic simulation with reactive driving agents. In: Proceedings of intelligent transportation systems 2001. IEEE, Oakland (CA) USA, pp 860–865
El hadouaj S, Drogoul A, Espié S (2000) How to combine reactivity and anticipation: the case of conflict resolution in a simulated road traffic. In: Moss S, Davidsson P (eds) Multi-agent-based simulation: second international workshop, MABS 2000, Boston; Revised and additional papers (Lecture notes in computer science, 1979). Springer, Berlin, pp 82–96
Esser J, Schreckenberg M (1997) Microscopic simulation of urban traffic based on cellular automata. Int J Mod Phys C 8(5):1025–1036
Fischer K, Chaib-draa B, Müller JP, Pischel M, Gerber C (1999) A simulation approach based on negotiation and cooperation between agents: a case study. IEEE Trans Syst Man Cybern C Appl Rev 29(4):531–545
Gambardella LM, Rizzoli A, Funk P (2002) Agent-based planning and simulation of combined rail/road transport. Simulation 78(5):293–303
Goldberg JB (2004) Operations research models for the deployment of emergency services vehicles. EMS Manage J 1(1):20–39
Gruer P, Hilaire V, Koukam A (2001) Multi-agent approach to modeling and simulation of urban transportation systems. In: Proceedings of the 2001 I.E. international conference on systems, man, and cybernetics (SMC 2001), vol 4. Tucson, 7–10 Oct 2001, pp 2499–2504
Hemelriajk C (2013) Animal social behaviour. Chapter 22 in this volume
Hensher DA, Ton TT (2000) A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice. Transport Res E 36(3):155–172
Holmgren J, Davidsson P, Ramstedt L, Persson JA (2012) TAPAS: a multi-agent-based model for simulation of transport chains. Simulation Model Pract Theory 23:1–18, Elsevier
Hunt JD, Gregor BJ (2008) Oregon generation 1 land use transport economic model treatment of commercial movements: case example. In: Hancock KL (Rap.) Freight demand modeling: tools for public-sector decision making; Summary of a conference, Washington, DC, 25–27 Sep 2006 (TRB conference proceedings, 40). Transportation research board, Washington, DC, pp 56–60
Jin X, Abdulrab H, Itmi M (2008) A multi-agent based model for urban demand-responsive passenger transport services. In: Proceedings of the international joint conference on neural networks (IJCNN 2008), Part of the IEEE world congress on computational intelligence, WCCI 2008, 1–6 June 2008, IEEE, Hong Kong, China, pp 3668–3675
Koorey G (2002) Assessment of rural road simulation modelling tools (Research report, 245). Transfund New Zealand, Wellington. http://ir.canterbury.ac.nz/bitstream/10092/1561/1/12591251_LTNZ-245-RuralRdSimulatnTools.pdf
Kumar S, Mitra S (2006) Self-organizing traffic at a malfunctioning intersection. J Artif Soc Soc Simul 9(4):3. http://jasss.soc.surrey.ac.uk/9/4/3.html
Lee S, Kim Y, Namgung M, Kim J (2005) Development of route choice behavior model using linkage of neural network and genetic algorithm with trip information. KSCE J Civil Eng vol 9, (4):321–327
Li T, Hofker F, Jansma F (2006) Passenger travel behavior model in railway network simulation. In: Perrone LF, Wieland FP, Liu J, Lawson BG, Nicol DM, Fujimoto RM (eds) Proceedings of the 2006 winter simulation conference, Baltimore
Mahmassani HS (ed) (2005) Transportation and traffic theory: flow, dynamics and human interaction. In: Proceedings of the 16th international symposium on transportation and traffic theory. Elsevier, Oxford, Great Britain
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. Appl Int 28(2):121–138
Meignan D, Simonin O, Koukam A (2007) Simulation and evaluation of urban bus-networks using a multiagent approach. Simulat Model Pract Theory 15:659–671
Nagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. J de Physique I 2(12):2221–2229
Ortúzar JD, Willumsen LG (2001) Modelling transport, 3rd edn. Wiley, Chichester
Panwai S, Dia H (2005) A reactive agent-based neural network car following model. In: Proceedings of the 8th international IEEE conference on intelligent transportation systems, Vienna, Austria, 13–16 Sept 2005. IEEE, Vienna, pp 375–380
Pelechano N, O’Brien K, Silverman B, Badler N (2005) Crowd simulation incorporating agent psychological models, roles and communication (Technical report). Center for Human Modeling and Simulation, University of Pennsylvania
Pursula M (1999) Simulation of traffic systems – an overview. J Geogr Inf Decis Anal 3(1):1–8
Ramstedt L (2008) Transport policy analysis using multi-agent-based simulation. Doctoral dissertation No 2008:09, School of Engineering, Blekinge Institute of Technology
Rindsfüser G, Klügl F (2007) Large-scale agent-based pedestrian simulation. In: Petta P et al (eds) MATES 2007, LNAI 4687, Leipzig, pp 145–156
Rossetti R, Bampi S, Liu R, Van Vleit D, Cybis H (2000) An agent-based framework for the assessment of driver decision-making. In: Proceedings of the 2000 I.E. intelligent transportation systems, IEEE, Dearborn, (MI) USA, IEEE, 387–392
Santos G, Aguirre BE (2004) A critical review of emergency evacuation simulation models. In: Peacock RD, Kuligowski ED (eds) Proceedings of the workshop on building occupant movement during fire emergencies. National Institute of Standards and Technology, Gaithersburg, 10–11 June 2004, pp 27–52
Schreckenberg M, Selten S (2004) Human behaviour and traffic networks. Springer, Berlin
Strader TJ, Lin FR , Shaw M (1998) Simulation of order fulfillment in divergent assembly supply chains. J Artif Soc Soc Simul 1(2). http://jasss.soc.surrey.ac.uk/1/2/5.html
Swaminathan J, Smith S, Sadeh N (1998) Modeling supply chain dynamics: a multiagent approach. Decis Sci J 29(3):607–632
Tapani A (2008) Traffic simulation modeling of rural roads and driver assistance systems. Doctoral thesis, Department of science and technology, Linköping University, Linköping
Terzi S, Cavalieri S (2004) Simulation in the supply chain context: a survey. Comput Ind 53(1):3–17
Toledo T (2007) Driving behaviour: models and challenges. Transport Rev 27(1):65–84
van der Zee DJ, van der Vorst JGAJ (2005) A modelling framework for supply chain simulation: opportunities for improved decision making. Decis Sci 36(1):65–95
Wahle J, Schreckenberg M (2001) A multi-agent system for on-line simulations based on real-world traffic data. In: Proceedings of the 34th annual Hawaii international conference on system sciences (HICSS’01). Hawaii, USA, IEEE
Williams I, Raha N (2002) Review of freight modelling. Final report, DfT Integrated Transport and Economics Appraisal, Cambridge
Zhang Q, Han B, Li D (2008) Modeling and simulation of passenger alighting and boarding movement in Beijing metro stations. Transport Res C 16:635–649
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Further Reading
Further Reading
For further information about traffic simulation we refer the interested reader to (Chung and Dumont 2009), (Tapani 2008), (Toledo 2007) and (Koorey 2002). Terzi and Cavalieri (2004) provide a review of supply chain simulation, while Williams and Raha (2002) present a review of freight modeling and simulation. For general information about transport modeling, we suggest to read (Ortúzar and Willumsen 2001). For further information on how agent technologies can be used in the traffic and transport area, see (Davidsson et al. 2005).
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Ramstedt, L., Krasemann, J.T., Davidsson, P. (2013). Movement of People and Goods. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93813-2_24
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