Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning

Aniekan Essien, Ilias Petrounias, Pedro Sampaio, Sandra Sampaio

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Abstract

Traffic parameter forecasting is critical to effective traffic management but is a challenging task due to the stochasticity of traffic flow characteristics, especially in urban road networks. Traffic networks can be affected by external factors, such as weather, events, accidents, and road construction networks. The impact of these factors can affect traffic flow parameters by influencing travel time, density, and operating speed. Although deep neural networks (DNNs) have recently shown promising signs in traffic prediction using big data, there still exists the issue of maximizing the use of the model capabilities by using big data sources. This paper proposes an improved urban traffic speed prediction approach involving input-level data fusion and deep learning. Motivated by deep learning prediction methods, we propose a Long Short- Term Memory Neural Network (LSTM-NN) for traffic speed prediction that combines traffic and weather datasets on an urban road network in Greater Manchester, United Kingdom. The experimental results substantiate the value of the approach when compared to the use of traffic-only data sources for traffic speed prediction.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
Place of PublicationKyoto, Japan
PublisherIEEE Computer Society
Number of pages8
ISBN (Electronic)9781538677896
ISBN (Print)9781538677896
DOIs
Publication statusPublished - 2019

Publication series

Name2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
PublisherIEEE
ISSN (Print)2375-933X
ISSN (Electronic)8237-3911

Keywords

  • Long short-term neural networks
  • data-fusion
  • deep learning
  • intelligent transportation systems (ITS)
  • traffic data science

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