Big Data Analytics in Freight ITS

This chapter examines how big data analytics enhances freight-oriented Intelligent Transport Systems (ITS) across data acquisition, storage, processing, and decision support. It synthesizes principal data sources such as telematics, Global Positioning System (GPS), radio-frequency identification (RFID), and platform records, and explains edge–cloud pipelines that convert heterogeneous inputs into structured, semi-structured, and unstructured datasets suitable for analysis. A three-tier analytical view is adopted, covering descriptive monitoring, predictive forecasting, and prescriptive optimization for routing, scheduling, and capacity reallocation. Urban and port-side applications illustrate gains in reliability, cost, and emissions performance, with case studies from Indian e-commerce operations and the Port of Rotterdam&s;s digital-twin ecosystem. The chapter also highlights risks around privacy, interoperability, cybersecurity, skills, and organizational adoption, outlining mitigation strategies. Overall, it positions big data as the backbone of adaptive, sustainable freight logistics.