Map matching serves as a fundamental component of various location-based services and trajectory mining applications, as it aligns GPS trajectory points with actual road networks. This process enhances the understanding of traffic flow patterns, provides insights into human behavior, and supports initiatives aimed at improving traffic conditions, thereby underscoring its scientific and practical significance. Among the various map matching methodologies, the Hidden-Markov Model (HMM) has emerged as a predominant approach, effectively addressing the challenge of matching accuracy by integrating historical vehicle travel data and utilizing observation and hidden state probabilities within a probabilistic framework. Nevertheless, despite the advancements in accuracy achieved through HMM, there remains potential for further enhancement of its computational efficiency to better accommodate large volumes of trajectory data. To address this issue, we propose the Fast HMM Map Matching Method Based on Road Network Simplification (FMM-RNS). Our method simplifies road network nodes to decrease query computations and employs bidirectional search along with segmentation of GPS trajectories, thereby significantly enhancing the computational efficiency of HMM-based map matching without compromising accuracy. Comprehensive experiments conducted on real road network and trajectory datasets demonstrate that FMM-RNS effectively reduces both the road search workload and the traversal of trajectory points, resulting in a substantial improvement in map matching efficiency when compared to state-of-the-art algorithms.