Book Chapter or Conference Paper Title

Using deep learning to predict short term traffic flow: a systematic literature review

Department

Department of Computer Science

Was this content written or created while at IBA?

Yes

Document Type

Conference Paper

Publication Date

11-2017

Author Affiliation

  • Usman Ali is PhD Scholar at Institute of Business Administration, Karachi
  • Tariq Mahmood is Assistant Professor at Institute of Business Administration, Karachi

Conference Name

International Conference on Intelligent Transport Systems

Conference Location

Hyvinkää, Finland

Conference Dates

29-30 November 2017

ISBN/ISSN

85049980152 (Scopus)

First Page

90

Last Page

101

Publisher

Springer, Cham

Abstract / Description

This paper systematically reviews Deep Learning-based methods for traffic flow prediction. We extracted 26 articles using a concrete methodology and reviewed them from two perspectives: first, the deep learning architecture used; and second, the datasets and data dimensions incorporated. Recent big data explosion caused by sensors, IoV, IoT and GPS technology needs traffic analytics using deep architectures. This survey reveals that the LSTM (Long Short-Term Memory) Neural Networks are the most commonly used architecture for short term traffic flow prediction due to their inherent ability to handle sequential data. Among the datasets, PeMS is the most commonly used for traffic flow prediction task. Today, Intelligent Transport Systems (ITS) are not limited to temporal data; spatial dimension is also incorporated along with weather data, and traffic sentiments from twitter, Facebook and Instagram to get better results. In the authors’ knowledge, this is the first deep learning review in ITS domain.

Citation/Publisher Attribution

Ali, U., & Mahmood, T. (2017, November). Using deep learning to predict short term traffic flow: A systematic literature review. In First International Conference on Intelligent Transport Systems (pp. 90-101). Springer, Cham.

Find in your library

Share

COinS