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

Seungyo Ryu, Dongseung Kim, Joongheon Kim

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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 number274
JournalApplied Sciences (Switzerland)
Issue number6
Publication statusPublished - 2020 Mar 1

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number: NRF-2018R1D1A1B07046195) and also by NRF-Korea (grant number: 2019R1A2C4070663).

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.


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

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


Dive into the research topics of 'Weather-aware long-range traffic forecast using multi-module deep neural network'. Together they form a unique fingerprint.

Cite this