Combining Weather Condition Data to Predict Traffic Flow

A GRU Based Deep Learning Approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Traffic flow prediction is an essential component of the intelligent transportation management system (ITS). This paper combines recurrent neural network and gated recurrent unit (GRU) to predict urban traffic flow considering weather conditions. Running results show that, under the review of weather influences, our method improves predictive accuracy and also decreases the prediction error rate. To our best knowledge, this is the first time that traffic flow is predicted in urban freeways in this particular way. This study examines it with respect to extensive weather influence under Gated Recurrent Unit (GRU) based deep learning framework.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1216-1219
Number of pages4
Volume2018-January
ISBN (Electronic)9781538619551
DOIs
StatePublished - Mar 29 2018
Event15th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017 - Orlando, United States
Duration: Nov 6 2017Nov 11 2017

Other

Other15th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
CountryUnited States
CityOrlando
Period11/6/1711/11/17

Fingerprint

Weather
Learning
Recurrent neural networks
Highway systems
Deep learning

Keywords

  • Big Traffic Data
  • Deep Learning
  • Deep Neural Network
  • Intelligent Transportation System

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Computer Science Applications
  • Information Systems

Cite this

Zhang, D., & Kabuka, M. R. (2018). Combining Weather Condition Data to Predict Traffic Flow: A GRU Based Deep Learning Approach. In Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017 (Vol. 2018-January, pp. 1216-1219). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.194

Combining Weather Condition Data to Predict Traffic Flow : A GRU Based Deep Learning Approach. / Zhang, Da; Kabuka, Mansur R.

Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1216-1219.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhang, D & Kabuka, MR 2018, Combining Weather Condition Data to Predict Traffic Flow: A GRU Based Deep Learning Approach. in Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1216-1219, 15th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017, Orlando, United States, 11/6/17. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.194
Zhang D, Kabuka MR. Combining Weather Condition Data to Predict Traffic Flow: A GRU Based Deep Learning Approach. In Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1216-1219 https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.194
Zhang, Da ; Kabuka, Mansur R. / Combining Weather Condition Data to Predict Traffic Flow : A GRU Based Deep Learning Approach. Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1216-1219
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