TL;DR
This paper introduces a parallel algorithm for Pruned Landmark Labelling tailored for DAGs, enhancing preprocessing efficiency by exploiting DAG topologies, particularly for public transport network graphs.
Contribution
It presents a novel parallel construction method for hub labels on DAGs, optimizing preprocessing by leveraging their topological structure.
Findings
Improved preprocessing speed for DAG-based hub labels
Effective parallelization leveraging DAG topologies
Potential applications in public transport network modeling
Abstract
We present a parallel variant of Pruned Landmark Labelling (PLL) that is optimised for the preprocessing of hub labels on directed acyclic graphs (DAGs). This method was developed during a seminar at the Karlsruhe Institute of Technology (KIT), focusing on time-expanded graphs that model public transport networks. The approach leverages the topological properties of DAGs to enable a novel parallel construction of hub labels.
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