With the increasing complexity of urban transportation systems and the growing demand for dynamic, real-time responsiveness, Time-Dependent Minimum Travel Time Queries (TD-MTTQs) in time-dependent road networks have become a core challenge in intelligent transportation system research. To address the trade-offs between preprocessing complexity and query efficiency in existing index-based methods for large-scale road network applications, this paper proposes a 4-hop index method, TD-TNR-CH. The core methodology involves establishing local indexes from each node to its nearest critical nodes (named transit nodes) through strategic critical node selection, while simultaneously constructing query tables associated with candidate sets between these critical nodes. This architecture enables rapid computation of medium-to-long distance queries through efficient index lookups, while ensuring high responsiveness for short-distance queries via TCH-based local searches. Extensive experimental results on large-scale real-world road networks demonstrate that our method exhibits superior scalability, achieving query efficiency of up to 103 times that of the fastest existing algorithms. Furthermore, it shows exceptional stability across queries of varying distances. Additionally, leveraging its parallelized architecture, TD-TNR-CH requires only approximately 30 minutes of preprocessing time for large-scale networks, significantly outperforming comparable methods in terms of preprocessing efficiency.