Given a set of trajectories on a road network, the goal of the All-Pair Network Trajectory Similarity (APNTS) problem is to calculate the similarity between all trajectories using the Network Hausdorff Distance. This problem is important for a variety of societal applications, such as facilitating greener travel via bicycle corridor identification. The APNTS problem is challenging due to the high cost of computing the exact Network Hausdorff Distance between trajectories in spatial big datasets. Previous work on the APNTS problem takes over 16 hours of computation time on a real-world dataset of bicycle GPS trajectories in Minneapolis, MN. In contrast, this paper focuses on a scalable method for the APNTS problem using the idea of row-wise computation, resulting in a computation time of less than 6 minutes on the same datasets. We provide a case study for transportation services using a data-driven approach to identify primary bicycle corridors for public transportation by leveraging emerging GPS trajectory datasets.