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

共 50 条

[1] Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction
Fouladgar, Mohammadhani
Parchami, Mostafa
Elmasri, Ramez
Ghaderi, Amir
[J]. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017, : 2251 - 2258
[2] Prediction of Road Traffic Flow Based on Deep Recurrent Neural Networks
Bartlett, Zoe
Han, Liangxiu
Trung Thanh Nguyen
Johnson, Princy
[J]. 2019 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI 2019), 2019, : 102 - 109
[3] Internet traffic prediction with deep neural networks
Jiang, Weiwei
[J]. INTERNET TECHNOLOGY LETTERS, 2022, 5 (02)
[4] Traffic Flow Prediction with Compact Neural Networks
Li, Yuhang
Wu, Celimuge
Yoshinaga, Tsutomu
Ji, Yusheng
[J]. IEEE 17TH INT CONF ON DEPENDABLE, AUTONOM AND SECURE COMP / IEEE 17TH INT CONF ON PERVAS INTELLIGENCE AND COMP / IEEE 5TH INT CONF ON CLOUD AND BIG DATA COMP / IEEE 4TH CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/CBDCOM/CYBERSCITECH), 2019, : 1072 - 1076
[5] Network-Wide Traffic Flow Dynamics Prediction Leveraging Macroscopic Traffic Flow Model and Deep Neural Networks
Yang, Hanyi
Yu, Wanxin
Zhang, Guohui
Du, Lili
[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024, 25 (05) : 4443 - 4457
[6] RPConvformer: A novel Transformer-based deep neural networks for traffic flow prediction
Wen, Yanjie
Xu, Ping
Li, Zhihong
Xu, Wangtu
Wang, Xiaoyu
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2023, 218
[7] Urban Traffic Flow Prediction with Deep Neural Network
Yang, Jin
[J]. SECURITY AND COMMUNICATION NETWORKS, 2022, 2022
[8] Passenger Flow Prediction in Traffic System Based on Deep Neural Networks and Transfer Learning Method
Ren, Yi
Chen, Xu
Wan, Sheng
Xie, Kunqing
Bian, Kaigui
[J]. 2019 4TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING (ICITE 2019), 2019, : 115 - 120
[9] Video source traffic flow prediction using neural networks
Bhattacharya, A
Parlos, AG
Atiya, AF
[J]. PROCEEDINGS OF THE 46TH IEEE INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS & SYSTEMS, VOLS 1-3, 2003, : 33 - 38
[10] Traffic Flow Prediction Using Graph Convolution Neural Networks
Agafonov, Anton
[J]. 2020 10TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST), 2020, : 91 - 95

← 1 2 3 4 5 →