Weather-aware long-range traffic forecast using multi-module deep neural network

Seungyo Ryu, Dongseung Kim, Joongheon Kim

Research output: Contribution to journalArticle

Abstract

This study proposes a novel multi-module deep neural network framework which aims at improving intelligent long-term traffic forecasting. Following our previous system, the internal architecture of the new system adds deep learning modules that enable data separation during computation. Thus, prediction becomes more accurate in many sections of the road network and gives dependable results even under possible changes inweather conditions during driving. The performance of the framework is then evaluated for diffierent cases, which include all plausible cases of driving, i.e., regular days, holidays, and days involving severe weather conditions. Compared with other traffic predicting systems that employ the convolutional neural networks, k-nearest neighbor algorithm, and the time series model, it is concluded that the system proposed herein achieves better performance and helps drivers schedule their trips well in advance.

Original languageEnglish
Article number10
JournalApplied Sciences (Switzerland)
Volume10
Issue number6
DOIs
Publication statusPublished - 2020 Mar 1

Keywords

  • Deep learning
  • Neural network
  • Traffic forecasting
  • Transportation network
  • Weather aware prediction

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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