Abstract One of the challenges for transportation decision makers is to identify capacity expansion in the network under the constraint of budget such that various objectives of the decision maker [such as total system travel time is minimized, social welfare (consumer surplus) is maximized, or total system emission is minimized], while accounting for the route choice behavior of users. Such type of investment decision making in the context of network design problems can be solved using optimization techniques. This type of optimization problem is particularly difficult to solve since two hierarchical decision making entities are involved (decision makers and road users). These two players have different objectives. The road users select routes such that their individual travel costs are minimized while the decision makers’ optimally seek to select capacity expansion projects in the network in such a way that planning objectives are achieved. The objective of this paper is to propose numerical methods and application algorithms such that optimal investment decisions are made in moderate and large transportation networks. The problem is formulated as a bi-level network design problem in which upper level determines the optimal capacity expansions of links and the lower level consist of the traditional Wardrop’s user equilibrium. The upper level provides a trial capacity expansion vector with additional network capacities. The lower level considers new link capacities for user equilibrium. The output from the lower level is a vector of link flows which is transferred to the upper level. This process is iterated using kth best algorithm till convergence. The upper level problem is solved using PSWARM algorithm and the solution for the lower level is obtained using an efficient static traffic assignment algorithm. Adequacy of the model is examined by first conducting numerical experiments using small networks from the literature and then using moderate to large scale networks. Results of numerical experiments indicates that proposed methodology from this study can be efficiently used for real-world applications in practice for transportation infrastructure capacity expansion decision making.