Big data applications in intelligent transport systems: a bibliometric analysis and review

Big Data applications have transformed Intelligent Transport Systems (ITS), enabling improvements in traffic management, safety, and efficiency. This study presents a bibliometric analysis and review of the recent advancements of big data applications in ITS. For this bibliometric analysis, the Scopus database was utilized due to its extensive resources. Various tools, such as RStudio, VOSviewer, Excel, and Python, were used to analyze data and identify trends, patterns, and relationships in the selected articles through performance analysis and science mapping. The study examined 447 articles published between 2014 and 2023. The analysis indicates that research in this field has experienced exponential growth annually, although it experienced setbacks in 2020 due to the global pandemic before regaining momentum. While the number of research publications has risen sharply, the slower growth in citations highlights the need for a greater focus on producing higher-quality research. Our investigation revealed that the most significant research efforts focused on traffic flow prediction, traffic anomaly prediction, traffic safety, the integration of big data with the Internet of Things (IoT) and the Internet of Vehicles (IoV). China, United States, and Canada were the primary contributors to this field, with China conducting the majority of studies. We summarized and critically reviewed the most cited papers, as well as those that present the most significant innovations in this field. We found that ethical, privacy, and security concerns related to the use of Big Data in ITS have received limited attention. This work aims to serve as a valuable resource for researchers and practitioners, encouraging innovation and the development of more effective and sustainable transportation solutions.