TRAFFIC FLOW PREDICTION BASED ON CASCADED ARTIFICIAL NEURAL NETWORK

被引：0

作者：

Zhang, Shaokun ^{[1
]}

Kang, Zejian ^{[1
]}

Hong, Zhiyou ^{[1
]}

Zhang, Zhemin ^{[1
]}

Wang, Cheng ^{[1
]}

Li, Jonathan ^{[1
,2
,3
]}

机构：

[1] Xiamen Univ, Sch Informat, Fujian Key Lab Sensing & Comp Smart Cites, Xiamen 361005, Fujian, Peoples R China

[2] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada

[3] Univ Waterloo, Fac Environm, WatMos Lab, Waterloo, ON N2L 3G1, Canada

来源：

IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年

关键词：

Traffic flow forecasting; artificial neural network; cascaded; feature analysis;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The prediction of traffic flow is of great significance for the prevention of accidents, the avoidance of congestion and the dispatch of command center. Considering the complexity of traffic data in reality, it is an extraordinarily challenging task to forecast accurately from historical patterns. In this paper, we propose a method based on the cascaded artificial neural network (CANN) to predict traffic flow at positions. In order to express the spatial correlation of traffic data, the actual road network distance is introduced in our model. The real-world data derived from video surveillance cameras in Xiamen is used in the experiment which is compared with five baselines. To the best of our knowledge, this is the first time that CANN is applied to forecast traffic flow. The experimental results demonstrate that the CANN method has superior performance. In addition, We also discuss the impact of some external factors such as temperature, weather and holidays on the prediction results.

引用

页码：7232 / 7235

页数：4

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