Abstract
In order to provide decision support for regional network congestion management, Granular computing theory was applied in traffic information processing. Traffic information granule and its granularity were defined, and then a methodology that provides a framework of congestion management for regional network was presented based on traffic information granular computing. A method was proposed for traffic state information granule construction based on extension set, and then the visualization traffic flow feature granule construction method was proposed. The numerical results of a case study indicates that the traffic state information granule can identify traffic state on line, and traffic flow feature granule can satisfy the needs of congestion management better. The results show that the existing traffic information processing methods could be integrated based on traffic information granular computing, and also can improve efficiency of congestion management.
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