Train crowding dominates crowd-shipping acceptance: evidence from Singapore’s MRT network

Postal collection logistics faces sustainability challenges requiring reduced vehicle emissions while managing labor constraints. This study examines transforming postal collection services into an entirely crowd-shipping-based operation using Singapore’s Mass Rapid Transit network, with car-based fallback during peak periods. Prior crowd-shipping research systematically excludes train crowding constraints, yet crowding affects operational feasibility when commuters transport large items. We employ stated preference experiment with Singapore residents to quantify acceptance under varying train crowding, financial compensation, and handling time conditions. Random-effects binary logit modeling estimates population-average acceptance probabilities, which inform vehicle routing optimization to assess system-level performance across temporal deployment strategies. Results reveal train crowding dominates acceptance behavior, creating operational constraints financial incentives cannot overcome. Optimal performance occurs during shoulder periods when moderate passenger demand reduces crowding. An entirely crowd-shipping-based postal operation is viable by strategically deploying train-based collection during low-crowding periods while utilizing existing car-based platforms during peak hours.