Long-term traffic time prediction using deep learning with integration of weather effect

Chih Hsin Chou, Yu Huang, Chian Yun Huang, Vincent Shin-Mu Tseng*

*Corresponding author for this work

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

Abstract

Traffic time prediction is a classical problem in intelligent transportation domain, which has attracted lots of attention from the research community in last three decades. The existing relevant works have been focused on how to predict the short-term traffic time for paths and roads. In fact, users may have the demand to know the future traffic time in advance as for making personal or commercial schedule. Long-term traffic time prediction is thus an emerging challenging task as there exist many complicated factors that may affect traffic situations, such as weather and congestion conditions. In this paper, we propose a novel deep learning-based framework named Deep Ensemble Stacked Long Short Term Memory (DE-SLSTM), which aims to solve the prediction bias during traffic congestion. To improve the model performance, we integrate the weather effect into the DE-SLSTM for predicting the long-term traffic time. Through a series of experiments, the proposed DE-SLSTM framework is verified to demonstrate excellent performance in terms of effectiveness. To the best of our knowledge, this is the first work on long-term traffic time prediction that considers deep learning techniques.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
EditorsQiang Yang, Zhiguo Gong, Sheng-Jun Huang, Zhi-Hua Zhou, Min-Ling Zhang
PublisherSpringer Verlag
Pages123-135
Number of pages13
ISBN (Print)9783030161446
DOIs
StatePublished - 1 Jan 2019
Event23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 - Macau, China
Duration: 14 Apr 201917 Apr 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11440 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
CountryChina
CityMacau
Period14/04/1917/04/19

Keywords

  • Ensemble model
  • Long-term prediction
  • Traffic time prediction
  • Weather effect

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  • Cite this

    Chou, C. H., Huang, Y., Huang, C. Y., & Tseng, V. S-M. (2019). Long-term traffic time prediction using deep learning with integration of weather effect. In Q. Yang, Z. Gong, S-J. Huang, Z-H. Zhou, & M-L. Zhang (Eds.), Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings (pp. 123-135). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11440 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-16145-3_10