Traffic management is improved in cutting-edge smart cities using technologies such as machine learning and deep learning to streamline daily tasks and boost productivity. However, traffic management still suffers from challenging issues such as poor traffic congestion prediction, lack of traffic fl...

Implementing Intelligent Transport System (ITS) presents many challenges despite its potential to enhance mobility, reduce congestion, and improve traffic management in urban regions. Previous research has focused on investigating the obstacles associated with implementing ITS in particular settings...

Intelligent traffic management systems (ITMS) are essential for safe and livable smart cities. However, achieving seamless interoperability between diverse devices and services is challenging due to the lack of universal open standards. This study examines different types of interoperability (syntac...

In recent decades the number of vehicles on the world’s roads has continued to increase. However, road capacity does not develop at the same rate, which generates a considerably increased congestion rate. To minimize this difficult problem, the researchers opted for intelligent and efficient use of ...

The incorporation of Artificial Intelligence algorithms in Intelligent Transportation Systems gives rise to new opportunities for a more sustainable urban mobility. However, one of the main challenges is to decide when and where these techniques should be applied; several options appear, such as clo...