Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Xiaochen Fan, Chaocan Xiang, Liangyi Gong, Xin He, Yuben Qu, Saeed Amirgholipour, Yue Xi, Priyadarsi Nanda, Xiangjian He

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)

Abstract

With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control. In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications. However, it is significantly challenging to fulfill the computation demands by the big traffic data with ever-increasing complexity and diversity. Deep learning, with its powerful capabilities in representation learning and multi-level abstractions, has recently become the most effective approach in many intelligent sensing systems. In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.

Original languageEnglish
Pages (from-to)240-260
Number of pages21
JournalCCF Transactions on Pervasive Computing and Interaction
Volume2
Issue number4
DOIs
Publication statusPublished - Dec 2020
Externally publishedYes

Keywords

  • Deep learning
  • Intelligent transportation system
  • Literature review
  • Pervasive computing
  • Traffic prediction

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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