This track focuses on the latest advancements in transportation analytics, emphasizing the integration of big data and machine learning techniques. Researchers are invited to present methodologies that enhance decision-making processes in transportation systems.

This session explores the application of predictive modeling techniques to improve traffic management and reduce congestion. Contributions should highlight case studies or novel approaches that leverage historical traffic data for real-time decision support.

This track examines the role of supply chain analytics in optimizing transportation logistics and operational efficiency. Papers should discuss frameworks that utilize data analytics to enhance supply chain performance in the transportation sector.

This session delves into the use of machine learning algorithms for fleet management, focusing on predictive maintenance and route optimization. Participants are encouraged to share insights on how these technologies can lead to cost reductions and improved service delivery.

This track investigates the impact of Internet of Things (IoT) technologies on real-time monitoring of transportation systems. Researchers are invited to present innovative solutions that utilize IoT data for enhanced operational insights and responsiveness.

This session addresses the integration of logistics analytics within the framework of smart city initiatives. Contributions should focus on how data-driven approaches can enhance urban mobility and logistics efficiency.