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Efficient Stochastic Routing in Path-Centric Uncertain Road Networks

Summary: Adopts a path-centric uncertain road-network model to capture trajectory-induced dependencies in travel-cost distributions and shows existing stochastic routing is inefficient there. Proposes two cost-to-go heuristics and a 'virtual path' abstraction to enable stochastic-dominance pruning under dependencies, yielding much faster stochastic routing on real trajectory datasets. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13509
Venue
VLDB
Year
2024
Pagerank
4.1945683e-05
Overall Rank
11,046 | 23.16%
DOI
10.14778/3681954.3681971

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Rank Citing Paper Year Venue Pagerank
10,629 TEAM: Topological Evolution-aware Framework for Traffic Forecasting 2025 VLDB 4.1945683e-05
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