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Abstract

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.

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Artificial Intelligence-Based Architecture for Real-Time Traffic Flow Management

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