Abstract Manager

As global efforts intensify towards environmental sustainability, the freight transportation sector confronts the critical challenge of reducing emissions while maintaining operational efficiency. This study addresses this challenge by modeling truck routing approaches that balance environmental sustainability with logistical efficiency in real-world scenarios. With a focus on mitigating the environmental footprint of freight transport, the objective is to develop a routing algorithm that considers sustainability and quantitatively assesses how different routing objectives impact emissions and travel time. The research seeks to provide practical insights into the trade-offs between travel time and emission in dynamic traffic conditions of road freight movement. Utilizing real-world telematics data, the methodology incorporates data-driven, time-dependent modeling of the road network traffic. Then, a label setting time-dependent shortest path algorithm is proposed to search for paths optimized for time, emission, and distance for any origin-destination pair in the road network. A case study is conducted in a large road network spanning seven major regional municipalities in the Greater Toronto and Hamilton Area. The trade-off analysis includes various types of realistic freight trip traversals in morning peak hours with different classes of trucks compared to the proposed optimized paths. The results reveal different trade-off profiles for each route, providing valuable insights for sustainable freight transport decision-making.