Combining weather condition data to predict traffic flow: A GRU-based deep learning approach

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Traffic flow prediction is an essential component of the intelligent transportation management system. This study applies gated recurrent neural network to predict urban traffic flow considering weather conditions. Running results show that, under the review of weather influences, their method improves predictive accuracy and also decreases the prediction error rate. To their best knowledge, this is the first time that traffic flow is predicted in urban freeways in this particular way. This study examines it with respect to extensive weather influence under gated recurrent unit-based deep learning framework.

Original languageEnglish (US)
Pages (from-to)578-585
Number of pages8
JournalIET Intelligent Transport Systems
Volume12
Issue number7
DOIs
StatePublished - Sep 1 2018

Fingerprint

learning
traffic
Recurrent neural networks
Highway systems
weather
motorway
prediction
neural network
management
weather condition
Deep learning
rate
method
urban traffic

ASJC Scopus subject areas

  • Transportation
  • Environmental Science(all)
  • Mechanical Engineering
  • Law

Cite this

Combining weather condition data to predict traffic flow : A GRU-based deep learning approach. / Zhang, Da; Kabuka, Mansur R.

In: IET Intelligent Transport Systems, Vol. 12, No. 7, 01.09.2018, p. 578-585.

Research output: Contribution to journalArticle

@article{e93af36f4c1244cf8c4399a41574bd16,
title = "Combining weather condition data to predict traffic flow: A GRU-based deep learning approach",
abstract = "Traffic flow prediction is an essential component of the intelligent transportation management system. This study applies gated recurrent neural network to predict urban traffic flow considering weather conditions. Running results show that, under the review of weather influences, their method improves predictive accuracy and also decreases the prediction error rate. To their best knowledge, this is the first time that traffic flow is predicted in urban freeways in this particular way. This study examines it with respect to extensive weather influence under gated recurrent unit-based deep learning framework.",
author = "Da Zhang and Kabuka, {Mansur R.}",
year = "2018",
month = "9",
day = "1",
doi = "10.1049/iet-its.2017.0313",
language = "English (US)",
volume = "12",
pages = "578--585",
journal = "IET Intelligent Transport Systems",
issn = "1751-956X",
publisher = "Institution of Engineering and Technology",
number = "7",

}

TY - JOUR

T1 - Combining weather condition data to predict traffic flow

T2 - A GRU-based deep learning approach

AU - Zhang, Da

AU - Kabuka, Mansur R.

PY - 2018/9/1

Y1 - 2018/9/1

N2 - Traffic flow prediction is an essential component of the intelligent transportation management system. This study applies gated recurrent neural network to predict urban traffic flow considering weather conditions. Running results show that, under the review of weather influences, their method improves predictive accuracy and also decreases the prediction error rate. To their best knowledge, this is the first time that traffic flow is predicted in urban freeways in this particular way. This study examines it with respect to extensive weather influence under gated recurrent unit-based deep learning framework.

AB - Traffic flow prediction is an essential component of the intelligent transportation management system. This study applies gated recurrent neural network to predict urban traffic flow considering weather conditions. Running results show that, under the review of weather influences, their method improves predictive accuracy and also decreases the prediction error rate. To their best knowledge, this is the first time that traffic flow is predicted in urban freeways in this particular way. This study examines it with respect to extensive weather influence under gated recurrent unit-based deep learning framework.

UR - http://www.scopus.com/inward/record.url?scp=85049377321&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049377321&partnerID=8YFLogxK

U2 - 10.1049/iet-its.2017.0313

DO - 10.1049/iet-its.2017.0313

M3 - Article

AN - SCOPUS:85049377321

VL - 12

SP - 578

EP - 585

JO - IET Intelligent Transport Systems

JF - IET Intelligent Transport Systems

SN - 1751-956X

IS - 7

ER -