@inproceedings{c084c394f4ec4af6badec6e06db30693,
title = "A deep architecture combining CNNS and GRBMS for traffic speed prediction",
abstract = "Short-term traffic speed prediction is an important technique for advanced traffic management systems. In this paper, a deep architecture combining convolutional neural networks (CNNs) and restricted Boltzmann machines (RBMs) with Gaussian units (GRBM-CNN) is proposed to predict short-term traffic speed; it combines the advantages of RBMs and CNNs and achieves a powerful capability to learn complex features of traffic data. Furthermore, this paper uses a graphics processing unit (GPU) to accelerate the learning process. Experimental results show that the proposed deep architecture has a lot of advantages over state-of-arts on the short-term traffic speed prediction task.",
keywords = "Convolutional Neural Networks (CNNs), Deep learning, Restricted Boltzmann Machines (RBMs), Traffic speed prediction",
author = "Huachun Tan and Zhiyu Zhong and Yuankai Wu and Xiaoxuan Chen and Jian Zhang",
note = "Publisher Copyright: {\textcopyright} ASCE.; 17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017 ; Conference date: 07-07-2017 Through 09-07-2017",
year = "2018",
doi = "10.1061/9780784480915.031",
language = "English",
series = "CICTP 2017: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation - Proceedings of the 17th COTA International Conference of Transportation Professionals",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "310--319",
editor = "Haizhong Wang and Jian Sun and Jian Lu and Lei Zhang and Yu Zhang and ShouEn Fang",
booktitle = "CICTP 2017",
address = "United States",
}