On-demand public transit systems require real-time computation of routes to ensure a user-friendly responsive service while also minimizing the vehicle miles traveled (VMT) of the fleet for increasing the profits of an operator. To ensure responsiveness, heuristic algorithms that rapidly generate near-optimal solutions are preferred over time-consuming exact computations. In order to further ensure the scalability of heuristic algorithms, especially to solve large problems, parallel computing techniques need to distribute the workload evenly across several partitions, while keeping passengers on similar routes with less detour in a single partition to reduce the VMT. However, existing works ignore these factors when partitioning the workload. This work proposes a road-network aware tree partitioning algorithm that not only considers the shortest path based routes but also the workloads to create balanced partitions in real-time. Experimental results on a real road-network show that the proposed algorithm outperforms a well-known unsupervised learning algorithm in terms of quality of results and runtime.