  This paper proposes multiple extensions to the popular bicriterion transit
routing approach -- Trip-Based Transit Routing (TBTR). Specifically, building
on the premise of the HypRAPTOR algorithm, we first extend TBTR to its
partitioning variant -- HypTBTR. However, the improvement in query times of
HyTBTR over TBTR comes at the cost of increased preprocessing. To counter this
issue, two new techniques are proposed -- a One-To-Many variant of TBTR and
multilevel partitioning. Our One-To-Many algorithm can rapidly solve profile
queries, which not only reduces the preprocessing time for HypTBTR, but can
also aid other popular approaches such as HypRAPTOR. Next, we integrate a
multilevel graph partitioning paradigm in HypTBTR and HypRAPTOR to reduce the
fill-in computations. The efficacy of the proposed algorithms is extensively
tested on real-world large-scale datasets. Additional analysis studying the
effect of hypergraph partitioning tools (hMETIS, KaHyPar, and an integer
program) along with different weighting schemes is also presented.
