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...

Full description

Saved in:
Bibliographic Details
Published in:International Conference on Big Data and Smart Computing pp. 328 - 331
Main Authors: Hongsuk Yi, HeeJin Jung, Sanghoon Bae
Format: Conference Proceeding
Language:English
Published: IEEE 01.02.2017
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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