Leveraging the passenger and freight spatiotemporal flow to optimize metro passenger-freight mixed transportation: a new mode for urban logistics systems

• Spatiotemporal estimation of trains' remaining freight capacity for resource allocation. • Cluster logistics outlets to identify freight loading/unloading stations. • Spatiotemporal freight demand forecasting captures dynamic route demand. • Integrate capacity estimation & demand prediction for 3 operation mode optimization models. To address the continuously growing urban logistics demand in megacities and alleviate traffic congestion in core urban areas, metro passenger-cargo mixed transportation, as a new transportation mode, shows great application prospects. First, a spatiotemporal-dependent estimation model for the remaining cargo transport capacity of trains was constructed based on the passenger flow across network sections. Second, logistics business outlets were clustered using GIS data and subway network layouts to determine cargo loading/unloading stations. Third, a spatiotemporal freight demand forecasting model was developed using historical OD (Origin-Destination) flow data. Subsequently, with hourly granularity, optimization models for train operation plans were formulated under three modes: passenger-cargo co-compartment, passenger-cargo co-train with separate compartments, and dedicated freight trains, leveraging the estimated remaining cargo capacity and predicted demand along the routes. Finally, an empirical analysis of Shanghai Metro Line 9 and SF Express revealed the following outcomes: the utilization rate of trains’ remaining freight capacity increased by 17.57%, daily revenue for the metro operator rose by 458,350 yuan, SF Express reduced its transportation costs by 229,175 yuan, urban traffic congestion was alleviated, and CO 2 emissions decreased. The research findings can promote the efficient integration of passenger and freight flows using the remaining capacity of metro trains, reduce logistics costs for enterprises, and mitigate urban congestion and environmental pollution.