mlsanityData to 7 Oct 2026

Origin-Destination Travel Time Oracle for Map-based Services

arXiv 2307.03048 ↗First posted 6 Jul 2023cs.LGcited by 20

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Abstract (arXiv)

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs. The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries. We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 22 of its 89 references that are in the map. First posted on arXiv 6 Jul 2023.

Builds on

22 of 89 references.

References · 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 9; 2025: 7; 2026: 2.

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  1. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 242
  2. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 66
  3. 2024TrajFM: A Vehicle Trajectory Foundation Model for Region and Task Transferabilitycited by 16
  4. 2023A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspectivecited by 6
  5. 2024DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimationcited by 6
  6. 2023A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospectscited by 5
  7. 2024A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Researchcited by 4
  8. 2025TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferabilitycited by 4
  9. 2024UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generationcited by 3
  10. 2024PTrajM: Efficient and Semantic-rich Trajectory Learning with Pretrained Trajectory-Mambacited by 3
  11. 2025Towards An Efficient and Effective En Route Travel Time Estimation Frameworkcited by 3
  12. 2025Spatio-Temporal Trajectory Foundation Model - Recent Advances and Future Directionscited by 3
8 more
  1. 2024Towards Effective Fusion and Forecasting of Multimodal Spatio-temporal Data for Smart Mobilitycited by 2
  2. 2025Towards Physics-informed Diffusion for Anomaly Detection in Trajectoriescited by 2
  3. 2024Link Representation Learning for Probabilistic Travel Time Estimationcited by 1
  4. 2025RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learningcited by 1
  5. 2025TrajMamba: An Efficient and Semantic-rich Vehicle Trajectory Pre-training Modelcited by 1
  6. 2026DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning -- Extended Versioncited by 1
  7. 2025FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learningcited by 0
  8. 2026MixTTE: Multi-Level Mixture-of-Experts for Scalable and Adaptive Travel Time Estimationcited by 0