Deep learning support for intelligent transportation systems

J. Guerrero-Ibañez, J. Contreras-Castillo, S. Zeadally

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Intelligent Transportation Systems (ITS) help improve the ever-increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in-depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

Original languageEnglish
Article numbere4169
JournalTransactions on Emerging Telecommunications Technologies
Issue number3
StatePublished - Mar 2021

Bibliographical note

Publisher Copyright:
© 2020 John Wiley & Sons, Ltd.

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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