Developing a real-time monitoring and alerting capability for traffic flow management

2016 ◽  
Author(s):  
Shin-Lai Alex Tien ◽  
James DeArmon ◽  
Hilton Bateman ◽  
Duane Freer ◽  
Patrick Somersall
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Real Time Monitoring ◽  
Flow Management ◽  
Traffic Flow Management

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

1998 ◽  
Vol 1651 (1) ◽  
pp. 53-58 ◽  
Author(s):  
Adel W. Sadek ◽  
Brian L. Smith ◽  
Michael J. Demetsky
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Traffic Flow ◽  
Traffic Management ◽  
Traffic Information ◽  
Case Based Reasoning ◽  
Flow Management ◽  
Real Time Traffic ◽  
Highway Network ◽  
Traffic Flow Management

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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