A bibliometric analysis of multi-source information fusion mechanisms in intelligent transportation big data: applications and efficiency perspectives - Li, Minsong; Isa, Mohd Adham; Khatibsyarbini, Muhammad; Abdull Hamed, Haza Nuzly; Chen, Donglin (Frontiers in Future Transportation, 2025)
The Multi-source Information Fusion (MSIF) mechanism and its applications involving big data in Intelligent Transportation Systems (ITS) represent a major research focus in current studies. This paper conducts a bibliometric analysis to summarize and evaluate developments in this domain. Initially, it categorizes the main application areas within ITS, then identifies the types and structures of data commonly involved, as well as frequently used datasets and evaluation metrics. It further explores specific MSIF applications in ITS, including road traffic management, traffic control, and transportation engineering computations. In particular, MSIF has demonstrated significant potential in enhancing transportation efficiency—such as reducing congestion, optimizing traffic signal coordination, and improving route planning—as well as improving energy efficiency by enabling eco-driving strategies and minimizing unnecessary fuel consumption. Based on the bibliometric findings, the study highlights current research challenges and key issues, while offering insights into future directions of MSIF development in intelligent transportation big data. This analysis aims to provide researchers and practitioners with a comprehensive overview and valuable guidance on the MSIF mechanism and its applications in ITS.