This study addresses the bilevel discrete network design problem (DNDP) with congestion, with special emphasis on fairness. The upper-level decision-maker (the network designer) selects a set of arcs to add to an existing transportation network, while the lower-level decision-makers (drivers) respond by choosing routes that minimize their individual travel times, resulting in user equilibrium. Most existing works in the literature primarily focus on minimizing the total travel time of all drivers and extending their solution approaches to other metrics remains a major research challenge. In this paper, we consider objectives related to individual travel times and fairness. To optimize such metrics, we propose a novel single-level reformulation of the DNDP based on strong duality of the lower-level problem.
To evaluate the performance of our method, we conduct numerical experiments on academic DNDP instances. We further demonstrate the practical relevance of our method through a case study on electric vehicle (EV) charging station capacity expansion. In this context, we introduce a fairness-based metric, the cost of sustainability, to quantify the inefficiency caused by EV adoption relative to a scenario where no charging is required. We then optimize expansion decisions to improve this metric. Experiments on a real-world road network in Quebec, incorporating existing public charging infrastructure, highlight the effectiveness and flexibility of our approach.