Long Term Traffic Prediction in Highway Using Parallel CNN

Donghyun Lim, Minhyeok Lee, Junhee Seok

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

Abstract

For navigation system, predicting future traffic conditions is crucial. To predict the traffic condition, statistical methods and neural network models have been studied. However, conventional methods have three limitations in which only the temporal properties are used, only narrow sections or time steps are predicted and not general road sections such as all section of highway but specific sections are used as test results. This paper proposes a parallel Convolutional Neural Network (CNN) that uses spatiotemporal properties and predicts for the next five hours and up to 400 km ranges in Korea's representative highway. Using a highway dataset, the proposed parallel CNN is trained and evaluated. As a result, the result of our model is improved by 10.6%, in terms of Root Mean Square Error (RMSE), compared to the conventional method. Moreover, in terms of the average of Average Speed Difference (ASD), the result of our model is improved by 63.5%.

Original languageEnglish
Title of host publication2020 IEEE 5th International Conference on Intelligent Transportation Engineering, ICITE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages107-110
Number of pages4
ISBN (Electronic)9781728194097
DOIs
Publication statusPublished - 2020 Sep
Event5th IEEE International Conference on Intelligent Transportation Engineering, ICITE 2020 - Beijing, China
Duration: 2020 Sep 112020 Sep 13

Publication series

Name2020 IEEE 5th International Conference on Intelligent Transportation Engineering, ICITE 2020

Conference

Conference5th IEEE International Conference on Intelligent Transportation Engineering, ICITE 2020
CountryChina
CityBeijing
Period20/9/1120/9/13

Keywords

  • deep learning
  • forecast up to five hours ahead
  • highway
  • spatialoral properties
  • traffic prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Automotive Engineering
  • Control and Optimization
  • Transportation

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