Data-Driven Methods and Challenges for Intelligent Transportation Systems in Smart Cities

This is the authors' abstract. We don't add key points for this paper.

As the Internet of Things (IoT) technology is seeing rapid advancements, the concept of creating smart cities is gaining huge popularity. One of the prominent sectors that can benefit from the rise in IoT technology and pave the way for smart cities is Intelligent Transportation Sys-tems (ITS). Data-driven approaches reliant on advancements in machine learning have gained wide popularity in the field of ITS. Such meth-ods facilitate solutions for problems in numerous ITS areas. This article aims to provide an analysis of some of the most notable works in four ITS categories: prediction and forecasting, detection, recognition, and safety. Different studies across these areas are reviewed, underlining the importance of data to ITS while focusing on the different architectures and technologies like machine learning used to advance ITS. Moreover, this article highlights the set of challenges faced by each area and proposes a potential solution for the main challenge.