An intelligent traffic responsive contraflow lane control system
- Resource Type
- Conference
- Authors
- Zhou, W.W.; Livolsi, P.; Miska, E.; Zhang, H.; Wu, J.; Yang, D.
- Source
- Proceedings of VNIS '93 - Vehicle Navigation and Information Systems Conference Vehicle navigation and informations systems Vehicle Navigation and Information Systems Conference, 1993., Proceedings of the IEEE-IEE. :174-181 1993
- Subject
- Transportation
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Control systems
Niobium
Road transportation
Traffic control
Optimal scheduling
Pattern matching
Scheduling algorithm
Delay
Processor scheduling
Computational efficiency
- Language
An intelligent-self learning dynamic optimal contraflow lane control system developed for the George Massey Tunnel in southern Greater Vancouver is introduced. A program was developed to permit the accurate estimation of realtime traffic demands. Online traffic data are sorted by a fuzzy modeling algorithm to identify the best matching pattern. A self learning mechanism is utilized to modify the predicted demand incrementally. An optimization algorithm is developed for online calculation of the optimal contraflow schedule based on the predicted demand. The total delay of both traffic approaches is minimized.