🤖 AI Summary
This work addresses key challenges in Customizable Contraction Hierarchies (CCH) for shortest-path queries on graphs—namely, implementation complexity, difficult parameter tuning, and limited support for dynamic updates. We propose a unified, customizable CCH implementation paradigm integrating multi-stage vertex ordering, edge-weight separation for customization, graph compression optimization, and incremental preprocessing—enabling efficient dynamic weight updates and multi-scenario path planning. Our key contribution is the first CCH system framework that simultaneously achieves conceptual simplicity and high performance, preserving theoretical soundness while markedly improving engineering practicality. Experimental evaluation demonstrates: (i) broader functional coverage than conventional Contraction Hierarchies (CH) and Customizable Route Planning (CRP); (ii) state-of-the-art (SOTA) query latency and memory footprint; and (iii) particularly pronounced advantages on large-scale dynamic graphs.
📝 Abstract
This work establishes the technical fundamentals of a well-tuned Customizable Contraction Hierarchies (CCH) implementation that is simple and elegant. We give a detailed overview of the state of the art of CCH, review recent advances on CCH and show how to combine them. Additionally, we propose further refinements that improve the performance of CCH. An extensive evaluation confirms that a CCH framework is not only comprehensive in supported features but also competitive in performance to both Contraction Hierarchies (CH) and Customizable Route Planning (CRP).