Optimization of urban rail transit passenger-freight co-transport considering cross-subsidy between passenger and freight

Abstract

Urban spatial restructuring and industrial upgrading have significantly increased urban passenger and freight demands, imposing dual pressures on air transport collection and distribution systems. On one hand, airport passenger and cargo volumes continue to rise. Meanwhile, accelerated urbanization has reduced road transport efficiency, with growing vehicle numbers outpacing road resource supply, leading to slower passenger and freight flows between airports and city centers. As a safe, fast, and highly punctual mode, urban rail transit plays an increasingly vital role in urban transportation. However, due to their late start, urban rail transit lines linked to airports experience passenger flows heavily influenced by flight arrivals, resulting in lower ridership during off-peak periods and surplus transport capacity. Therefore, utilizing these airport-connected urban rail transit lines to provide cargo distribution services between airports and city centers presents a potential solution. This approach can harness surplus line capacity to enhance distribution efficiency while mitigating operational losses from insufficient ridership and boosting revenue. This research addresses the urban rail transit passenger-freight co-transport challenge by proposing an optimization model that simultaneously considers passenger comfort and penalties for unmet freight demand. The model fulfills freight demand allocation by adjusting train timetables while satisfying train operational and capacity constraints. To solve this optimization problem, an enhanced greedy algorithm is designed, implementing dual strategies: prioritizing freight demands with higher freight revenue for transport and assigning transport tasks to trains with fewer waiting passenger counts at stations. This ensures passenger service quality while increasing freight revenue and completing demand allocation. Concrete numerical experiments, including comparisons with GUROBI solver results, validate the effectiveness of the model and algorithm. The study offers a novel approach for optimizing urban rail transit timetables and developing enterprise operational strategies.