With　the　advancement　of　economic　globalization,　multimodal　transportation　is　gradually　gaining　importance　in　the　field　of　logistics.　This　paper　addresses　the　multimodal　transportation　route　planning　problem　with　uncertain　node　loads　and　proposes　a　chance-constrained　model　based　on　fuzzy　sets　to　balance　the　costs,　carbon　emissions,　and　customer　satisfaction.　Additionally,　an　improved　cooperative　coevolution　algorithm　is　introduced　to　solve　the　problem.　The　proposed　model　is　applied　to　a　real-world　case　to　validate　its　effectiveness,　and　through　algorithm　comparison,　the　performance　of　the　improved　algorithm　is　demonstrated.　The　findings　indicate　that　node　loads　at　varying　confidence　levels　exert　a　noteworthy　impact　on　both　delivery　time　and　waiting　time　for　shipments.　Furthermore,　a　more　intricate　transportation　network　results　in　extended　waiting　times,　highlighting　the　heightened　importance　of　accounting　for　uncertain　node　loads　and　transportation　schedules.　©　2024　ICIC　International.