A Campus Traffic Congestion Detecting Method Based on BP Neural Network
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Xiong, Shengwu
Xiang, Jianwen
Mao, Jingjing
Liu, Mianfang
Zhao, Yang
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Wuhan, China
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Abstract
This paper presents a novel method for detecting the campus traffic congestion by combining BP neural network with campus traffic congestion descriptor. In this method, road occupancy rate is proposed and proved to be the most effective descriptor among other descriptors of traffic congestion level in campus. The campus traffic congestion levels are divided into three phases based on three-phase traffic theory. Experimental results show that the proposed method is capable of detecting campus traffic congestion.
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Proceedings - 2015 2nd International Symposium on Dependable Computing and Internet of Things, DCIT 2015
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Networking and communications
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Yu, X; Xiong, S; Xiang, J; Mao, J; Liu, M; Zhao, Y, A Campus Traffic Congestion Detecting Method Based on BP Neural Network, 2015 2nd International Symposium on Dependable Computing and Internet of Things (DCIT), 2016, pp. 93-100