A systematic survey on big data and artificial intelligence algorithms for intelligent transportation system

Abstract

Rapid urbanization and globalization have resulted in intolerable congestion and traffic, necessitating the investigation of Intelligent Transportation Systems (ITS). ITS employs advanced technologies to address modern transportation challenges, aiming to create smarter, faster, and safer transportation networks. Increased data availability and the emergence of Artificial Intelligence (AI) and Big Data have enabled ITS gain significant attention in recent years. The integration of AI and Big Data contributes significantly to ITS development, optimizing traffic planning, forecasting, and management, and concurrently reducing transportation costs by enhancing the performance of public transportation, ride-sharing, and smart parking. This survey paper performs a systematic study and comprehensive exploration of the synergistic integration of Big Data and Artificial Intelligence (AI) in Intelligent Transportation Systems (ITS). By elucidating the underlying principles, the paper emphasizes the transformative potential of these technologies in addressing contemporary challenges in transportation. It innovatively delves into specific ITS application domains, including traffic flow forecasting, congestion management, and intelligent routing, offering a detailed analysis of how the amalgamation of Big Data and AI enhances efficiency across various facets of modern transportation systems. The survey not only highlights the benefits of this integration in terms of efficient traffic planning and reduced transportation costs but also delves into the associated challenges, including data collection, data privacy, security, computational complexity, and algorithmic scalability. Furthermore, it contributes valuable insights by proposing potential solutions and suggesting future research directions to enhance effectiveness of big data and AI algorithms in the realm of ITS.