Freight Transport

Freight transport research intensely focuses on optimizing efficiency and sustainability within increasingly complex global supply chains. Current studies employ diverse modeling approaches, including agent-based simulations, machine learning (e.g., XGBoost, ensemble learning, deep learning for object detection), and discrete-event simulations to analyze factors like container handling strategies, autonomous platooning, and the impact of population density on truck flows. These analyses aim to improve transportation planning, reduce costs and emissions, and address challenges like driver shortages through innovations such as teleoperated driving. The findings directly inform infrastructure investment, logistics optimization, and the development of more efficient and environmentally responsible freight systems.