Sustainable impact analysis of freight pooling strategies on city crowdsourcing logistics platform
Abstract
This study investigates the effects of freight pooling strategies on urban crowdsourcing logistics, focusing on economic, social, and environmental outcomes. Utilising a mixed-integer linear programming model with an adaptive large neighbourhood search algorithm, our goal is to optimise the cost-efficiency of the freight pooling system. Real-world delivery and driver trajectory data from a major Chinese crowdsourcing logistics platform, along with high-resolution vehicle telematics data, validate our model in five scenarios, each defined by distinct cost coefficients reflecting diverse stakeholder priorities. Results show potential for up to a 21.3% reduction in carbon emissions, a 28.3% decrease in truck activity spatial coverage, and a 7.6% increase in available drivers. However, deadheading trips, due to order consolidation into fewer vehicles without an increase in overall demand, could offset maximum carbon reduction benefits by 16.4%. Other effects on customers’ and drivers’ welfare are explored for a comprehensive quantitative assessment of freight pooling strategies’ sustainability benefits.
Simon Hu
Assistant Professor
