Real-time traffic flow management has recently emerged as one of the promising approaches to alleviating congestion. This approach uses real-time and predicted traffic information to develop routing strategies that attempt to optimize the performance of the highway network. A survey of existing approaches to real-time traffic management indicated that they suffer from several limitations. In an attempt to overcome these, the authors developed an architecture for a routing decision support system (DSS) based on two emerging artificial intelligence paradigms: case-based reasoning and stochastic search algorithms. This architecture promises to allow the routing DSS to ( a) process information in real time, ( b) learn from experience, ( c) handle the uncertainty associated with predicting traffic conditions and driver behavior, ( d) balance the trade-off between accuracy and efficiency, and ( e) deal with missing and incomplete data problems.
Artificial Intelligence-Based Architecture for Real-Time Traffic Flow Management
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 1651 , 1 ; 53-58
1998-01-01
Article (Journal)
Electronic Resource
English
Artificial Intelligence-Based Architecture for Real-Time Traffic Flow Management
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