Enablers of Data-Driven Intelligent Transportation Systems for Sustainable Smart Cities (SDG 11): A Systematic Literature Review

Abstract

Traditional transportation systems have evolved into more advanced systems, namely the Intelligent Transportation Systems (ITS). But despite this transformation, modern transportation systems still face problems. Challenges such as traffic congestion, accidents, and high emissions are yet to be effectively solved. Gathered data show that around 10% of the world's emissions come from the transportation sector, and approximately 1.3 million deaths happen every year from road accidents. These recurring issues demonstrate the necessity for ITS to be enhanced and evolved to gain the ability to solve those problems. Numerous existing literatures have covered the topic of data-driven ITS, with the primary focus on explaining the technological side of transportation innovations. In this context, ITS has become a foundation of smart city development, enabling data-driven and sustainable mobility systems. However, there remains a knowledge gap as no research specifically learned about the enablers behind those successful ITS implementations. Studying enablers helps to create knowledge on how previous ITS implementations were successfully launched. This can aid transportation providers to replicate those functional outcomes for the creation of smarter mobility solutions. This study uses the Systematic Literature Review (SLR) methodology, where 22 documents were collected from databases using relevant search words before being processed using a five-step framework. This study contributes by suggesting a taxonomy of ITS enablers, grouped into three categories: technological, institutional, and human. The long-term goal of this study is to improve how ITS responds to real-world transportation challenges and to fully eliminate those problems using knowledge from previous studies.