Study of Traffic Flow Controlled with Independent Agent-Based Traffic Signals
Dealing with urban traffic is a highly complex task since it involves the coordination of many actors. Traditional approaches attempt to optimize traffic signal control for a particular vehicle density; the main disadvantage lies in the fact that traffic changes constantly. Managing traffic congestion seems to be a problem of adaptation rather than of optimization. In this work we present an agent-based traffic simulator which represents a traffic grid with two-way roads of three exclusive lanes per direction, with intersections regulated by signals. We study the repercussions on traffic flow of simple parametric behaviours when each light operates independently. A dominance analysis is applied to compare the strategies.
KeywordsTraffic Signal Intelligent Transportation System Vehicle Density Vehicle Detector Left Turn
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- 3.Robertson, D.I.: Traffic Models and Optimum Strategies of Control: A Review. Proceedings on Traffic Control Systems 1, 276–289 (1979)Google Scholar
- 4.Webster, F.: Traffic signal settings. In: HMSO (1958)Google Scholar
- 5.Wunderlich, R., Elhanany, I., Urbanik, T.: A stable longest queue first signal scheduling algorithm for an isolated intersection. In: IEEE International Conference on Vehicular Electronics and Safety, pp. 1–6 (2007)Google Scholar
- 7.Sims, A., Dobinson, K.: SCAT-The Sydney Co-ordinated Adaptive Traffic System–Philosophy and Benefits. In: International Symposium on Traffic Control Systems, vol. 2 (1979)Google Scholar
- 8.Henry, J., Farges, J., Tuffal, J.: The PRODYN real time traffic algorithm. In: Proceedings of the 4th Conference on Control in Transportation Systems, vol. 2(1), p. 305 (1984)Google Scholar
- 10.Wilensky, U., et al.: NetLogo (1999), http://ccl.northwestern.edu/netlogo
- 11.Fonseca, C., Fleming, P., et al.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423 (1993)Google Scholar