We propose a scalable nested Benders decomposition (BD) framework for single-leader, multi-follower bilevel optimization problems. The proposed framework is applicable to bilevel optimization problems in which each follower solves a linear program and is particularly well suited for instances involving a large number of followers. By identifying the upper-level decisions as complicating variables, the method exploits the separable structure of the lower-level problems, which decouple by follower once the upper-level decisions are fixed. The algorithm employs a two-level, nested BD scheme comprised of an outer and an inner decomposition. To accelerate convergence, we incorporate two complementary strategies: (i) a closest Benders cuts scheme to reduce the number of outer iterations; and (ii) parallel computing techniques to solve the inner BD subproblems efficiently. The framework is implemented on a distributed computing architecture with code optimized for high-performance execution on large-scale instances. We demonstrate the practical utility of the approach by applying it to a mixed-integer bilevel formulation of a line-planning problem with integrated passenger routing in public transportation systems, involving over 50 million variables. Numerical experiments indicate significant computational improvements over the monolithic baseline and exhibit strong scaling performance as the number of processors increases.