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- W566386985 abstract "This paper introduces a revolutionary approach for an intelligent adaptive traffic signal control (IATSC) system which is capable for both on-line networks and corridor traffic signal optimal control. This system is developed based on intelligent fuzzy modeling and fuzzy neural algorithms, and traffic delay minimization algorithms. It consists of two major parts: off-line and on-line. In the off-line part, traffic flow patterns are identified using a fuzzy clustering algorithm. Traffic control parameters (signal timings) are optimized. Any new traffic patterns are self-learned using a fuzzy neural algorithm. In the on-line part, real time traffic data is acquired through field traffic controllers. Dynamic traffic flow patterns are analyzed and predicted by an adaptive prediction algorithm. A best fitting traffic pattern which has an off-line designed timing plan is then identified promptly by a fuzzy neural algorithm. A timing plan related to the identified pattern is then immediately executed to control the real time traffic flows. A pilot system has been implemented on Trans Canada Highway at the city of Duncan since mid April, 1996. Results have shown significant achievements on traffic delay reductions and overall performances. An average of more than 35% traffic delay reduction has been approved." @default.
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- W566386985 date "1996-01-01" @default.
- W566386985 modified "2023-09-22" @default.
- W566386985 title "INTELLIGENT ADAPTIVE TRAFFIC SIGNAL CONTROL" @default.
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