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Intelligent traffic control decision support system
Almejalli, Khaled A. Dahal, Keshav P. Hossain, M. Alamgir
Almejalli, Khaled A.
Dahal, Keshav P.
Hossain, M. Alamgir
Publication Date
2007
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© 2007 Springer-Verlag. Reproduced in accordance
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Abstract
When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.
Version
Accepted manuscript
Citation
Almejalli, K. A., Dahal, K. P. and Hossain, M. A. (2007) Intelligent
traffic control decision support system. In: Applications of evolutionary computing.
Proceedings of the EvoWorkshops 2007 (EvoTransLog Workshop), Valencia,
Spain, April 11-13, 2007. Heidelberg: Springer. pp. 688-701.
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Type
Conference paper