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Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models

Summary: STHMMs model sparse, spatio-temporally correlated travel-cost time series from GPS data, handling missing entries and cross-segment dependencies. Parameter learning under sparsity and heterogeneity enables near-future travel-cost inference for eco-routing; validated on large GPS datasets for efficiency and accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10739
Venue
VLDB
Year
2013
Pagerank
4.5435639e-05
Overall Rank
8,305 | 42.23%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
2,680 Finding Semantics in Time Series 2011 SIGMOD 8.3234371e-05
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