Cloud-Based Big Data Processing Architectures for Intelligent Public Transportation Systems
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Abstract
Employment of intelligent public transportation systems is intensifying with the adoption of related concepts such as smart cities, Internet of Things, big data, and artificial intelligence. These approaches require processing of Big Data generated by the growing number of smartphones, digital tickets, transit vehicles, surveillance cameras, and abundance of IoT sensors deployed in operations. Various cloud-based Big Data processing architectural paradigms have emerged. When adopting such paradigms, a public transportation operator must choose the one that best aligns with its needs. A thorough comprehension of the major cloud-based Big Data processing paradigms is vital for transit authorities to enhance data-driven decision-making and accelerate the development of intelligent public transportation. Therefore, a representation of the primary classes of cloud-centric Big Data processing paradigms is presented, along with an analysis of the processing models employed, the elements of Big Data governance management, the requirements for intelligent public transport workloads, and the relevance of cloud-based Big Data processing systems for public transit.
The importance of Big Data processing for intelligent management of public transportation is then examined. Subsequently, these considerations are related to key components of the data lifecycle—architecture, data ingestion, and integration. Architectural patterns and integration points are analyzed toward defining a high-level architectural model that fosters seamless interaction among the different components.
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