Computing time-optimal shortest paths, in road networks, is one of the most popular applications of Artificial Intelligence. This problem is tricky to solve because road congestion affects travel times. The state-of-the-art in this area is an algorithm called Time-dependent Contraction Hierarchies (TCH). Although fast and optimal, TCH still suffers from two main drawbacks: (1) the usual query process uses bi-directional Dijkstra search to find the shortest path, which can be time-consuming; and (2) the TCH is constructed w.r.t. the entire time domain T, which complicates the search process for queries q that start and finish in a smaller time period Tq ⊂ T. In this work, we improve TCH by making use of time-independent heuristics, which speed up optimal search, and by computing TCHs for different subsets of the time domain, which further reduces the size of the search space. We give a full description of these methods and discuss their optimality-preserving characteristics. We report significant query time improvements against a baseline implementation of TCH.