This paper presents a rapid tree-based reoptimization framework for solving traffic assignment problems in many critical rapid decision making applications. In recent years, regional transportation decision makers and planning organizations are faced with many important decision-making situations with significantly changed origin-destination (O-D) demand patterns, partially due to pattern shifts in land use and emerging transportation modes such as shared bikes and shared vehicles. Thus, there is a critical need for a faster-than-real-time decision-making support system for enabling more informed planning processes. In our approach, we propose a new reoptimization method to recalculate new paths according to baseline traffic assignment results when responding to a new set of traffic demands or supply scenarios. Through smart indexing of previously-calculated traffic assignment outputs, our proposed algorithm can generate new network flow distributions quickly with satisfactory convergence performance. Finally, numerical experiments are performed to demonstrate the adaptive converging behavior and computational efficiency of our tree-based reoptimization algorithm.