A sustainable crowd shipping model using public transport for low-carbon urban logistics

• Developed a dynamic programming-based transport mode selection model integrating trucks, private cars, and trains for optimized urban parcel delivery. • Incorporated time constraints, congestion limits, and environmental cost factors , encouraging sustainable transport choices. • Conducted empirical validation using GIS and logistics datasets from Osaka, Japan, ensuring real-world applicability. • Identified that the proposed CCTS model can reduce CO 2 emissions by up to 80% , lower delivery costs, and improve last-mile efficiency. • Demonstrated the feasibility of leveraging public transport and parcel lockers to enhance urban freight resilience and sustainability. This study proposes a novel approach to sustainable urban logistics through a crowdsourced co-modality transportation system (CCTS) integrated with public transport networks. The research introduces a dual-layer multi-modal CCTS framework that optimally combines conventional delivery services with crowd shipping, leveraging existing passenger transport infrastructure to reduce environmental impacts. A dynamic programming model is developed to optimize mode selection, incorporating multiple transport modes in the context of physical internet, congestion constraints, train capacity, and carbon footprint considerations. By employing heuristic search methods for parcel locker placement, the study enhances last-mile delivery efficiency. Using Osaka as a case study, simulation results demonstrate that engaging commuters as delivery agents and integrating smart parcel lockers can significantly reduce urban freight emissions – achieving up to 80% carbon savings in specific scenarios. These findings highlight the potential of public transport-based crowd shipping to enhance delivery efficiency while contributing to cleaner and more sustainable cities.