Data-Driven Approaches in Transportation Engineering

Modern transportation evolves through data analytics, leveraging machine learning and simulation tools for optimized traffic flow, network efficiency, and urban mobility planning. Case studies of smart cities and ITS illustrate how data drives predictive modeling and informed decision-making in the mobility sector. Modern transportation systems rely heavily on data analytics to improve efficiency, safety, and sustainability. The application of big data and machine learning enables predictive modeling and intelligent infrastructure management. Data-Driven Approaches in Transportation Engineering focuses on analytical techniques for traffic flow analysis, network optimization, and urban mobility planning. It discusses data collection methods, simulation tools, and decision-making algorithms. The book also highlights case studies involving smart cities and intelligent transportation systems (ITS). Bridging engineering and data science, it offers valuable insights for planners, researchers, and policymakers in the mobility sector.