Modelling Congestion Evolution in Railway Marshalling Yard Inbound Operations Under Irregular Train Arrivals

Railway marshalling yard inbound operations are affected by irregular train arrivals, finite receiving-yard capacity, service interruptions, and boundary states carried across operating days. This study develops a hybrid fluid–queueing and simulation framework for analyzing congestion evolution in the arrival–technical-operation–hump-disassembly process. The framework integrates continuous arrival-input construction, boundary-state stability validation (PSSBV), and daily gated integer discrete-event simulation (DGDES). Circular kernel density estimation and observed train-level sequences represent non-stationary arrivals; PSSBV determines operationally reasonable initial conditions; and DGDES captures finite-capacity admission, FIFO outside holding, parallel technical operations, hump disassembly, and service-interruption windows. The framework is evaluated under deterministic and stochastic service-time conditions using continuous-operation data from a large Chinese marshalling yard and field-observed occupancy and waiting-time indicators. The results show that congestion is driven less by daily arrival volume alone than by the interaction of concentrated arrivals, pre-disassembly backlog, residual in-yard occupancy, and insufficient hump-disassembly clearance capacity. The simulated outside holding rate follows fluctuations in the field-based track–time load ratio and is closely associated with disassembly waiting time, system sojourn time, and pre-disassembly queue length. Lagged analysis indicates that previous-day saturation and residual workload can increase next-day outside holding risk. Stochastic service times produce similar high-risk patterns while increasing variability and tail-risk exposure. The proposed threshold–probability diagnostic framework supports rapid congestion-risk identification and dispatching-oriented control under continuous and uncertain operating conditions.