The existing online mapping systems process many user route queries simultaneously, yet solve each independently, using typical route guidance solutions. These route recommendations are presented as optimal, but often this is not truly the case, due to the effects of competition users experience over the resulting experienced routes, a phenomenon referred to in Game Theory as a Nash Equilibrium. Additionally, route plans of this nature can result in poor utilization of the road network from a system-optimizing perspective as well. In this paper, we introduce an enhanced approach for route guidance, motivated by the relevance of a system optimal equilibrium strategy, while also maintaining some fairness to the individual. With this approach the objective is to optimize the global road network utilization (as measured by, e.g., mobility, or global emissions) by selecting from a set of generally fair user route alternatives in a batch setting. For the first time, we present an approximate, anytime algorithm based on Monte Carlo Tree Search and Eppstein's Top-K Shortest Paths algorithm to solve this complex dual optimization problem in real-time. This approach attempts to identify and avoid the potentially harmful network effects of sub-optimal route combinations. Experiments show that mobility optimization over real road networks of Rye and Golden, Colorado in a microscopic traffic simulation with a network congestion-minimizing objective can achieve considerable mobility improvement for users, as observed by their effective travel time improvement up to 12% with some consideration of route fairness.