SPTTE: A Spatiotemporal Probabilistic Framework for Travel Time Estimation

• URL: http://arxiv.org/abs/2411.18484v1
• Date: Wed, 27 Nov 2024 16:28:54 GMT
• Title: SPTTE: A Spatiotemporal Probabilistic Framework for Travel Time Estimation
• Authors: Chen Xu, Qiang Wang, Lijun Sun,
• Abstract summary: Real-world trip data are often temporally sparse and unevenly distributed.<n> SPTTE incorporates an RNN-based temporal Gaussian process parameterization to regularize sparse observations and capture temporal dependencies.<n> Evaluations on real-world datasets demonstrate that SPTTE outperforms state-of-the-art probabilistic methods by over 10.13%.
• Score: 21.092166159353702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate travel time estimation is essential for navigation and itinerary planning. While existing research employs probabilistic modeling to assess travel time uncertainty and account for correlations between multiple trips, modeling the temporal variability of multi-trip travel time distributions remains a significant challenge. Capturing the evolution of joint distributions requires large, well-organized datasets; however, real-world trip data are often temporally sparse and spatially unevenly distributed. To address this issue, we propose SPTTE, a spatiotemporal probabilistic framework that models the evolving joint distribution of multi-trip travel times by formulating the estimation task as a spatiotemporal stochastic process regression problem with fragmented observations. SPTTE incorporates an RNN-based temporal Gaussian process parameterization to regularize sparse observations and capture temporal dependencies. Additionally, it employs a prior-based heterogeneity smoothing strategy to correct unreliable learning caused by unevenly distributed trips, effectively modeling temporal variability under sparse and uneven data distributions. Evaluations on real-world datasets demonstrate that SPTTE outperforms state-of-the-art deterministic and probabilistic methods by over 10.13%. Ablation studies and visualizations further confirm the effectiveness of the model components.

Related papers

• ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations [18.64802090861607]
  ProGen Pro provides a robust solution that effectively captures dependencies while managing uncertainty. Our experiments on four benchmark traffic datasets demonstrate that ProGen Pro outperforms state-of-the-art deterministic probabilistic models.
  arXiv  Detail & Related papers  (2024-11-02T14:37:30Z)
• Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification [59.81904428056924]
  We introduce SMASH: a Score MAtching estimator for learning markedPs with uncertainty quantification. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of markedPs through score-matching. The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification.
  arXiv  Detail & Related papers  (2023-10-25T02:37:51Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• Probabilistic Learning of Multivariate Time Series with Temporal Irregularity [25.91078012394032]
  temporal irregularities, including nonuniform time intervals and component misalignment. We develop a conditional flow representation to non-parametrically represent the data distribution, which is typically non-Gaussian. The broad applicability and superiority of the proposed solution are confirmed by comparing it with existing approaches through ablation studies and testing on real-world datasets.
  arXiv  Detail & Related papers  (2023-06-15T14:08:48Z)
• Spatio-temporal Diffusion Point Processes [23.74522530140201]
  patio-temporal point process (STPP) is a collection of events accompanied with time and space. The failure to model the joint distribution leads to limited capacities in characterizing the pasthua-temporal interactions given events. We propose a novel parameterization framework, which learns complex spatial-temporal joint distributions. Our framework outperforms the state-of-the-art baselines remarkably, with an average improvement over 50%.
  arXiv  Detail & Related papers  (2023-05-21T08:53:00Z)
• Assessment of Spatio-Temporal Predictors in the Presence of Missing and Heterogeneous Data [23.280400290071732]
  Deep learning approaches achieve outstanding predictive performance in modeling modern data, despite increasing complexity and scale. evaluating the quality of predictive models becomes more challenging as traditional statistical assumptions often no longer hold. This paper introduces a residual analysis framework designed to assess the optimality of temporal-temporal predictive neural models.
  arXiv  Detail & Related papers  (2023-02-03T12:55:08Z)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting [30.277213545837924]
  Many classical statistical models often fall short in handling the complexity and high non-linearity present in time-series data. In this work, we consider the time-series data as a random realization from a nonlinear state-space model. We use particle flow as the tool for approximating the posterior distribution of the states, as it is shown to be highly effective in complex, high-dimensional settings.
  arXiv  Detail & Related papers  (2021-06-10T21:49:23Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.