Assessing Urban Freight Tours: A Machine Learning and Life Cycle Sustainability Assessment Approach for Logistics Management
将机器学习与生命周期可持续性评估结合，分析氢燃料和电动轻型商用车相比传统燃油车的环境影响，发现氢燃料车可减少生态系统和健康损害58%和61%，为城市绿色物流政策提供依据。
ABSTRACT The main aim of this study is to assess urban freight tours through integrating machine learning with the Life Cycle Sustainability Assessment (LCSA). The research captures supply chain operations using the Gradient Boosting Regressor (GBR) model with real‐time data from surveys and Global Positioning System (GPS) tracking. These predictions were analysed using LCSA to assess the sustainability impacts of Hydrogen Fuel Light Commercial Vehicles (HFLCVs) and Electric Light Commercial Vehicles (ELCVs) compared to traditional fuel‐based vehicles. HFLCVs show remarkable reductions in ecosystem and health damage by 58% and 61%, indicating substantial environmental and health benefits. Findings suggest that strategic investment in hydrogen‐fuel and electric LCVs can significantly decrease operational costs and environmental impacts, making them crucial for advancing sustainable urban logistics. This research highlights the benefits and possibilities of using an integrated data‐driven approach to achieve urban sustainability, thus creating an urgency to shift policies favouring green urban freight systems.