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)
Incoming Non-self Citations Over Time
Authors
- 1. Bin Yang
- 2. Chenjuan Guo
- 3. Christian S. Jensen
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,388 | Anytime Stochastic Routing with Hybrid Learning | 2020 | VLDB | 8.9132902e-05 |
| 5,026 | AutoCTS: Automated Correlated Time Series Forecasting | 2022 | VLDB | 5.7528419e-05 |
| 9,079 | Path Cost Distribution Estimation Using Trajectory Data | 2017 | VLDB | 4.400728e-05 |
| 11,046 | Efficient Stochastic Routing in Path-Centric Uncertain Road Networks | 2024 | VLDB | 4.1945683e-05 |
| 11,242 | Effective and Efficient Route Planning Using Historical Trajectories on Road Networks | 2023 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
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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