To investigate the impact of transfer node service priority on multimodal transport path selection under carbon trading price uncertainty, this study models carbon price fluctuations using a “carbon K-line” distribution and quantifies service priority via cargo time value, optimising node service processes for multi-task handling. An interval robust optimisation model is formulated to minimise total transport costs (including transport, time, cargo time value, and carbon emission costs), subject to constraints such as service priority, transfer capacity limits, and mixed time windows. The model is solved using a catastrophe-adaptive genetic algorithm with Monte Carlo sampling. Case studies of three transport tasks reveal that (1) incorporating service priority alters transport paths, reducing total cargo time value loss by 12.64% and decreasing comprehensive costs by 2.26%; (2) carbon price uncertainty increases rail transport distance share by 10.86% on average and raises carbon emission cost proportions by 0.23%, ultimately increasing comprehensive costs by 3.48%. These findings assist multimodal operators in holistically evaluating cargo types, shipper requirements, and carbon markets. By forecasting carbon prices and implementing service priority, stakeholders can select low-carbon intermodal paths that balance cost efficiency, service priority, and emission reduction, thereby supporting sustainable freight transport decision-making.
Hu et al. (Sat,) studied this question.