Deep learning support for intelligent transportation systems
J. Guerrero‐Ibañez, Juan Contreras‐Castillo, Sherali Zeadally
Abstract
Abstract 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.