Deep Neural Networks for Traffic Flow Prediction

被引：0

作者：

Yi, Hongsuk ^{[1
]}

Jung, HeeJin ^{[1
]}

Bae, Sanghoon ^{[2
]}

机构：

[1] Korea Inst Sci & Technol Informat, Supercomp Ctr, Daejeon, South Korea

[2] Pukyong Natl Univ, Dept Spatial Informat Engn, Busan, South Korea

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP) | 2017年

关键词：

traffic prediction; deep learning neural networks; transportation big data;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Traffic flow prediction is an essential function of traffic information systems. Conventional approaches, using artificial neural networks with narrow network architecture and poor training samples for supervised learning, have been only partially successful. In this paper, a deep-learning neural-network based on TensorFlow (TM) is suggested for the prediction traffic flow conditions, using real-time traffic data. Until now, no research has applied the TensorFlow (TM) deep learning neural network model to the estimation of traffic conditions. The suggested supervised model is trained by a deep learning algorithm, which uses real traffic data aggregated every five minutes. Results demonstrate that the model's accuracy rate is around 99%.

引用

页码：328 / 331

页数：4

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