Flexible adaptation to the heterogeneous distribution of travel demand is a challenging issue in the transit network design and planning. Limited-stop service is a strategy used to deal with this problem. In this paper, a bilevel mathematical model is proposed in which the upper-level model minimizes the total fleet unused capacity of limited-stop services, while the objective function of the lower-level model is to minimize total expected travel time for all passengers. Since this model is operator-oriented, some constraints are embedded in it in order to prevent excessive fall of the passengers' service level. Actually, the upper level is a mixed integer linear programming model; but because of NP-hardness and the lower level model, a heuristic algorithm is developed in order to solve the proposed bi-level model. It's worth mentioning that this heuristic approach is based on iterated local search, simulated annealing, and tabu search metaheuristics. Finally, a vast variety of numerical experiments are carried out that are based on some virtual origin-destination demand matrices generated according to different characteristics. Computational results show that by replacing the local service with the set of limited stop services, the fleet unused capacity can be reduced by up to 48%.

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