Abstract
Multi-agent systems allow the simulation of complex phenomena that cannot be easily described analytically. As an efficient tool, the agen t-oriented traffic models have emerged to investigate vehicular traffic behaviors. In this article, a new agent-based traffic simulation model is proposed for solving the traffic simulation problems. A vehicle with the driver is represented as a composite autonomous agent in this model. Different from the classical car-following principles, vehicle-agent moving approaches are proposed instead of particle-hopping techniques. This model defines reasonable acceleration and deceleration rates at any certain condition. In this simulation, this can offer a natural, even cognitive way to follow the leader and switch lane. The simulation results have verified that this model has achieved more realistic results without loss of accuracy than those obtained from the cellular automata traffic models. This model can provide better simulation performance than the traditional vehicle-following models which are used to investigate the nonequilibrium traffic flow. A comparison of the real flow with the simulated freeway flow and lane capacity highlights the validness of this model.
Similar content being viewed by others
References
Chowdhury D, Santen L, Schadschneider A. Statistical physics of vehicular traffic and some related systems. Phys Rep, 2000, 329: 199–329
Pipes L A. An operational analysis of traffic dynamics. J Appl Phys, 1953, 24: 274–281
Gazis D C, Herman R, Potts R B. Car-following theory of steady-state traffic flow. Oper Res, 1959, 7: 499–505
Gazis D C, Herman R, Rothery R W. Nonlinear follow-the-leader models of traffic flow. Oper Res, 1961, 9: 545–567
Newell G F. A theory of platoon formation in tunnel traffic. Oper Res, 1959, 7: 589–598
Newell G F. Nonlinear effects in the dynamics of car following. Oper Res, 1961, 9: 209–229
Edie L C. Car-following and steady-state theory for noncongested traffic. Oper Res, 1961, 9: 66–76
Gipps P G. A behavioural car-following model for computer simulation. Transport Res B-Meth, 1981, 15: 105–111
Bando M, Hasebe K, Nakayama A, et al. Dynamical model of traffic congestion and numerical simulation. Phys Rev E, 1995, 51: 1035–1042
Treiber M, Hennecke A, Helbing D. Congested traffic states in empirical observations and microscopic simulations. Phys Rev E, 2000, 62: 1805–1824
Nagel K. Particle hopping models and traffic flow theory. Phys Rev E, 1996, 53: 4655–4672
Biham O, Middleton A A, Levine D. Self-organization and a dynamical transition in traffic-flow models. Phys Rev A, 1992, 46: 6124–6127
Fukui M, Ishibash Y. Traffic flow in 1D cellular automaton model including cars moving with high speed. J Phys Soc Japan, 1996, 65: 1868–1870
Schreckenberg M. Cellular automation models and traffic flow. J Phys A, 1993, 26: 679–683
Daganzo C F. In traffic flow, cellular automata = kinematic waves. Transport Res B-Meth, 2006, 40: 396–403
Helbing D. Traffic and related self-driven many-particle systems. Rev Mod Phys, 2001, 73: 1067–1141
Jiang R, Wu Q S. Phase transition at an on-ramp in the Nagel-Schreckenberg traffic flow model. Physica A, 2006, 366: 523–529
Sun T, Wang J F. A traffic cellular automata model based on road network grids and its spatial and temporal resolution’s influences on simulation. Simul Model Pract Th, 2007, 15: 864–878
Nagel K, Schreckenberg M. A Cellular Automaton Model for Free-way Traffic. J Phys I France, 1992, 2: 2221–2229
Lu F, Zhou C H, Wan Q. Implementation of a feature-based non-planar database for urban traffic networks (in Chinese). Acta Geod Cartogr Sinica, 2002, 31: 182–186
Bham G H, Benekohal R F. A high fidelity traffic simulation model based on cellular automata and car-following concepts. Transport Res C-Emer, 2004, 12: 1–32
Wahle J, Bazzan A L C, Klugl F, et al. The impact of real-time information in a two-route scenario using agent-based simulation. Transport Res C-Emer, 2002, 10: 399–417
Adler J L, Satapathy G, Manikonda V, et al. A multi-agent approach to cooperative traffic management and route guidance. Transport Res B-Meth, 2005, 39: 297–318
Cetin N, Nagel K, Raney B, et al. Large-scale multi-agent transportation simulations. Comput Phys Commun, 2002, 147: 559–564
Balmer M, Axhausen K W, Nagel K. Agent-based demand-modeling framework for large-scale microsimulations. Transport Res Rec, 2006, 1985: 125–134
Hernandez J Z, Ossowski S, Garcia-Serrano A. Multiagent architectures for intelligent traffic management systems. Transport Res C-Emer, 2002, 10: 473–506
Hidas P. Modelling vehicle interactions in microscopic simulation of merging and weaving. Transport Res C-Emer, 2005, 13: 37–62
Bazzan A L C. A distributed approach for coordination of traffic signal agents. Auton Agent Multi-Ag, 2005, 10: 131–164
Davidsson P, Henesey L, Ramstedt L, et al. An analysis of agent-based approaches to transport logistics. Transport Res C-Emer. 2005, 13: 255–271
Burmeister B, Doormann J, Matylis G. Agent-oriented traffic simulation. Trans Soc Comput Simul. 1997, 14: 79–86
Rossetti R J F, Bampi S, Liu R H, et al. An agent-based framework for the assessment of drivers’ decision-making. In: Intelligent Transportation Systems, 2000 Proceedings, 2000 IEEE. Dearborn (MI), 2000. 387–392
Dia H. An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transport Res C-Emer, 2002, 10: 331–349
Ehlert P A, MRothkrantz L J M. Microscopic traffic simulation with reactive driving agents. In: Intelligent Transportation Systems, 2001 Proceedings, 2001 IEEE. Oakland, 2001. 860–865
Mandiau R, Champion A, Auberlet J M, et al. Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation. Appl Intell, 2008, 28: 121–138
Wei Y, Han Y, Fan B. In Agent-oriented urban traffic micro simulation system. In: Industrial Technology, 2008 ICIT, 2008 IEEE International Conference on. Chengdu, 2008. 1–7
Zhang F, Li J L, Zhao Q X. Single-lane traffic simulation with multi-agent system. In: Intelligent Transportation Systems, 2005 Proceedings, 2005 IEEE. Vienna, 2005. 1183–1187
Treiterer J. Investigation of Traffic Dynamics by Aerial Photogrammetry Techniques. Final Report EES278. 1975
Jula H, Kosmatopoulos E B, Ioannou P A. Collision avoidance analysis for lane changing and merging. IEEE Transactions on Vehicular Technology, 2000, 49: 2295–2308
Chowdhury D, Wolf D E, Schreckenberg M. Particle hopping models for two-lane traffic with two kinds of vehicles: Effects of lane-changing rules. Physica A, 1997, 235: 417–439
Barlovic R, Esser J, Froese K, et al. Online traffic simulation with cellular automata. eds. Brilon W. In: Traffic and Mobility SimulationEconomics Environment. Berlin: Springer, 1999. 117–134
Naranjo J E, Gonzalez C, Reviejo J, et al. Adaptive fuzzy control for inter-vehicle gap keeping. IEEE Transactions on Intelligent Transportation Systems, 2003, 4: 132–142
Aycin M F, Benekohal R F. Linear acceleration car-following model development and validation. Transport Res Rec, 1998, 1644: 10–19
Greenberg H. An analysis of traffic flow. Oper Res, 1959, 7: 79–85
Huber M J. Effect of temporary bridge on parkway performance. Highw Res Board Bull, 1957, 167: 63–74
Drake J, Schofer J, May A D. A statistical analysis of speed-density hypotheses. In: Proceedings of 3rd international symposium on theory of traffic flow. New York: Vehicular Traffic Science, Elsevier, 1967. 112–117
Underwood R T. Speed, Volume and Density Relationships. In: Quality and Theory of Traffic Flow. New Haven, Conn: Bureau of Highway Traffic, Yale University, 1961. 141–187
Greenshields B D, Bibbins J R, Channing W S, et al. A study of traffic capacity. Highw Res Board Proceedings. 1934, 14: 448–477
Wagner P. Traffic simulations using cellular automata: Comparison with reality. eds. Wolf D E, Schreckenberg M, Bachem A. In: Traffic and Granular Flow. Singapore: World Scientic, 1996. 193–200
Sasaki M, Nagatani T. Transition and saturation of traffic flow controlled by traffic lights. Physica A, 2003, 325: 531–546
Huang D W, Huang W N. Traffic signal synchronization. Phys Rev E, 2003, 67: 056124
Jiang R, Wu Q S. A stopped time dependent randomization cellular automata model for traffic flow controlled by traffic light. Physica A, 2006, 364: 493–496
Tomer E, Safonov L, Madar N, et al. Optimization of congested traffic by controlling stop-and-go waves. Phys Rev E, 2002, 65: 065101
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Dai, J., Li, X. Multi-agent systems for simulating traffic behaviors. Chin. Sci. Bull. 55, 293–300 (2010). https://doi.org/10.1007/s11434-009-0230-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11434-009-0230-3