A local selection rule based on a fractional solution of the expected instance preserves the expected maximum matching size under sufficient spread and yields near-optimal global matchings with small local budgets on ride-hailing data.
Improved bounds for online stochastic matching
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.DS 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Extensions to delay-aware multimodal journey planning frameworks (ULTRA, CSA, RAPTOR) deliver 1.9-4.2x speedups in earliest-arrival queries, competitive bicriteria performance with higher accuracy, and better scaling as the delay buffer Delta grows.
citing papers explorer
-
Stochastic Matching via Local Sparsification
A local selection rule based on a fractional solution of the expected instance preserves the expected maximum matching size under sufficient spread and yields near-optimal global matchings with small local budgets on ride-hailing data.
-
Fast and Memory Efficient Multimodal Journey Planning with Delays
Extensions to delay-aware multimodal journey planning frameworks (ULTRA, CSA, RAPTOR) deliver 1.9-4.2x speedups in earliest-arrival queries, competitive bicriteria performance with higher accuracy, and better scaling as the delay buffer Delta grows.