Using Big Data for Predictive Maintenance in Transportation Systems

Abstract

The economic and social health of contemporary urban centers is greatly dependent on the transportation industry. Transportation infrastructure must be dependable and efficient because any disruptions can have a domino effect on urban mobility as a whole. Predictive maintenance, facilitated by the analysis of big data, gives a chance to proactively address maintenance needs and minimize service interruptions. The use of big data analytics for predictive maintenance in transportation systems is examined in this chapter. It starts by going over the special data sources that are available in the transportation industry, such as sensor data from infrastructure, cars, and traffic control systems. It explores the essential phases of the predictive maintenance procedure, encompassing data gathering, analysis, modeling, and the production of practical maintenance insights. The application of big data-driven predictive maintenance in various transportation contexts—such as public transportation fleets, road infrastructure, and rail networks—is demonstrated through several case studies.