Deep Neural Networks for traffic flow prediction

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-ne...

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Vydáno v:	International Conference on Big Data and Smart Computing s. 328 - 331
Hlavní autoři:	Hongsuk Yi, HeeJin Jung, Sanghoon Bae
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.02.2017
Témata:	Analytical models Big Data Data models deep learning neural networks Machine learning Optimization Real-time systems traffic prediction Transportation transportation big data
ISSN:	2375-9356
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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™ is suggested for the prediction traffic flow conditions, using real-time traffic data. Until now, no research has applied the TensorFlow™ 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%.
ISSN:	2375-9356
DOI:	10.1109/BIGCOMP.2017.7881687