Authors:
Safa Batita
;
Achraf Makni
and
Ikram Amous
Affiliation:
National School of Electronics and Telecommunications (ENET’Com), University of Sfax, MIRACL Laboratory, Airport Road, km 4, B.P. 1088, 3018 Sfax, Tunisia
Keyword(s):
Intelligent Transportation Systems, Databases, Artificial Intelligence, Advanced Driver Assistance Systems.
Abstract:
This paper presents an examination of data engineering within Intelligent Transportation Systems (ITS), focusing on integrating advanced technologies such as Real-Time Databases (RT-DBs), Graph Databases (GDBs), and Artificial Intelligence (AI) to improve ITS capabilities. The decision to focus on database systems and AI in this paper is based on their crucial roles in shaping modern transportation systems and offers a comprehensive view of the technological framework influencing ITS. Through an extensive review of existing literature, the paper explores how these solutions synergistically contribute to data collection, organization, processing, and extraction of value from various ITS data. The paper analyzes the transformative impact of real-time data management in connected vehicle systems and the efficacy of GDBs in capturing complex relationships within intelligent transportation networks. Additionally, it assesses the adaptability of AI in various ITS applications, including tr
affic prediction, driver assistance, and accident analysis. Despite their benefits, the paper discusses persistent challenges related to system complexity, interoperability, data management, and model accuracy, which impact the widespread deployment of ITS. Furthermore, the paper presents recommendations for addressing these challenges and emphasizes research directions that require further exploration, underscoring the importance of intelligent and efficient transportation worldwide.
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