Artificial intelligence-based architecture for real-time traffic flow management

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