Capacity Expansion Problem for Large Urban Transportation Networks

A traffic network design problem attempts to find optimal network expansion policies under budget constraints. This can be formulated as a bilevel optimization problem: the upper level determines the optimal link capacity expansion vector and the lower level determines the link flows subject to user equilibrium conditions. The upper level is a capacity expansion problem which minimizes the total system cost and can be solved using any optimization algorithm. In the present study, genetic algorithm (GA) is used in the upper level because of its modeling simplicity and ability to handle large problems. The proposed model is first applied to a small sized network and then to a medium sized test network and the results are compared with other existing solution approaches. The sensitivity analysis of the model is performed by designing the networks at different demand levels. The resilience of the solution when demand increases the design demand is also carried out. Finally, the network design for the city of Pune, India was taken as a case study. This is a large sized network having 1,131 links and 370 nodes. The capacity expansion is carried out under various budget scenarios and the results are discussed. This study shows the potential of GA to obtain a high quality solution for large network design problems