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Transit network simulation models are often used for performance andretrospective analysis of urban rail systems, taking advantage of theavailability of extensive automated fare collection (AFC) and automated vehiclelocation (AVL) data. Important inputs to such models, in addition toorigin-destination flows, include passenger path choices and train capacity.Train capacity, which has often been overlooked in the literature, is animportant input that exhibits a lot of variabilities. The paper proposes asimulation-based optimization (SBO) framework to simultaneously calibrate pathchoices and train capacity for urban rail systems using AFC and AVL data. Thecalibration is formulated as an optimization problem with a black-box objectivefunction. Seven algorithms from four branches of SBO solving methods areevaluated. The algorithms are evaluated using an experimental design thatincludes five scenarios, representing different degrees of path choicerandomness and crowding sensitivity. Data from the Hong Kong Mass TransitRailway (MTR) system is used as a case study. The data is used to generatesynthetic observations used as "ground truth". The results show that theresponse surface methods (particularly Constrained Optimization using ResponseSurfaces) have consistently good performance under all scenarios. The proposedapproach drives large-scale simulation applications for monitoring andplanning.

Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data