Generative Adversarial Network-Based Regional Epitaxial Traffic Flow Prediction

Yan Kang, Jinyuan Li, Shin Jye Lee, Hao Li*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Predicting urban traffic flow is of big significant to traffic management and public security. However, with the continuous expansion of urban areas and the development of data acquisition technology, new types of traffic data are characterized by wide spatial distribution, high timeliness and large data volume. Traffic flow forecasting requires high cost and related domain knowledge. Therefore, it has become an urgent research topic to properly use a small amount of traffic data to efficiently construct a traffic prediction model. In this paper, we propose a generative adversarial network-based traffic flow prediction method called RT-GAN which is the real-time prediction of traffic flows in the surroundings area according to the traffic information in the central area. The combination of gated convolution and dilated convolution can capture the traffic information in the near and far regions and perform feature fusion to achieve real-time prediction. Experiments on the Beijing and New York traffic flow data sets show that our method outperforms others, providing a potential solution to practical applications.

Original languageEnglish
Title of host publicationAdvances in Natural Computation, Fuzzy Systems and Knowledge Discovery - Volume 2
EditorsYong Liu, Lipo Wang, Liang Zhao, Zhengtao Yu
PublisherSpringer
Pages804-814
Number of pages11
ISBN (Print)9783030325909
DOIs
StatePublished - 1 Jan 2020
Event15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019 - Kunming, China
Duration: 20 Jul 201922 Jul 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1075
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019
CountryChina
CityKunming
Period20/07/1922/07/19

Keywords

  • Feature fusion
  • Generative Adversarial Network
  • Real-time
  • Traffic flow

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