A flexible evaluation and design model for transport network capacity under demand variability is proposed. The future stochastic demand is assumed to follow a normal distribution. Traveler path choice behavior is assumed to follow the probit stochastic user equilibrium. The network reserve capacity is used to evaluate the performance of the network. Since the future demand is stochastic, the reserve capacity is measured by possible increases in both mean and standard deviation (SD) of the base demand distribution. The proposed model therefore represents the flexibility of the network in its robustness to origin-destination demand variation (i.e., high SD). The proposed model can also determine an optimal network design to maximize the reserve capacity of the network for both the mean and the SD of the increased demand distribution. The implicit programming approach is applied to solve the optimization problem. Sensitivity analysis is adopted to provide all necessary derivatives. The model and algorithm are tested with a hypothetical network to illustrate the merits of the proposed model.