2014Advanced materials researchOpen access

Research on the Algorithm of Automatic Urban Traffic Congestion Identification

Ku Bo, Ping Sun, Yun Ke

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Abstract

A method of traffic congestion identification based on traffic assignment model and SA-PC method is presented through analysis of municipal traffic congestion characteristic. Whether traffic congestion occurs or not is considered as a special classification problem. If the branch road of city is not affected by the signal light, the traffic situation is divided into tow parts: congestion and unimpeded. Using data associated with the traffic parameter for congestion and non congestion, an incremental SA-PC method is trained to detect whether traffic congestion occur or not. Experimental results based on microcosmic traffic simulation indicate that this method is not only feasible but also effective.

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What this paper is about

A method of traffic congestion identification based on traffic assignment model and SA-PC method is presented through analysis of municipal traffic congestion characteristic. Whether traffic congestion occurs or not is considered as a special classification problem. If the branch road of city is not affected by the signal light, the traffic situation is divided into tow parts: congestion and unimpeded. Using data associated with the traffic parameter for congestion and non congestion, an incremental SA-PC method is trained to detect whether traffic congestion occur or not. Experimental results based on microcosmic traffic simulation indicate that this method is not only feasible but also effective.

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Available abstract

A method of traffic congestion identification based on traffic assignment model and SA-PC method is presented through analysis of municipal traffic congestion characteristic. Whether traffic congestion occurs or not is considered as a special classification problem. If the branch road of city is not affected by the signal light, the traffic situation is divided into tow parts: congestion and unimpeded. Using data associated with the traffic parameter for congestion and non congestion, an incremental SA-PC method is trained to detect whether traffic congestion occur or not. Experimental results based on microcosmic traffic simulation indicate that this method is not only feasible but also effective.

Key concepts: Traffic congestion, Traffic congestion reconstruction with Kerner's three-phase theory, Identification (biology), Traffic optimization, Computer science, Transport engineering, Traffic bottleneck, Network traffic control

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