No / The operator of a traffic control centre has to select the most appropriate traffic control action or combination of actions in a short time to manage the traffic network when non-recurrent road traffic congestion happens. 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 actions that need to be considered during the decision making process. The identification of suitable control actions 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 actions for a number of control measures in a complicated situation is very time-consuming. This chapter presents an intelligent method for the real-time identification of road traffic actions which assists the human operator of the traffic control centre in managing the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural networks, and genetic algorithms. The system employs a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a genetic algorithm (GA) 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 in Saudi Arabia. The results obtained for the case study are promising and demonstrate that the proposed approach can provide an effective support for real-time traffic control.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/2292 |
| Date | January 2007 |
| Creators | Almejalli, Khaled A., Dahal, Keshav P., Hossain, M. Alamgir |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Book chapter, No full-text available in the repository |
| Relation | http://www.springerlink.com/content/ur546632356ul606/ |
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