research-article

MTTPRE: a multi-scale spatial-temporal model for travel time prediction

Authors:

Weihao Jiang, and

Shiliang PuAuthors Info & Claims

SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems

November 2022

Article No.: 51, Pages 1 - 10

https://doi.org/10.1145/3557915.3560986

Published: 22 November 2022 Publication History

Abstract

Travel time prediction is a critical task in intelligent transportation system and location-based service. Existing studies build models based on the features extracted from trajectories, but few of them consider the sparsity of trajectory data from both temporal and spatial dimensions, as well as the spatial structure and heterogeneity. To address these issues, we propose a novel Multi-scale spatial-temporal model for Travel Time Prediction, abbreviated as MTTPRE. Specifically, the study area is represented as a flexible Voronoi graph according to a variable-sized partition scheme and the missing features on it are recovered via a spatial-temporal context-based method. Subsequently, a geospatial network with POI information is established to represent the spatial structure based on the Voronoi graph. Next, the multi-dimensional traffic condition features and graph-trajectory-POI multilevel features are extracted as spatial-temporal features. Finally, these features are fed into a hierarchical multi-task learning layer to complete the travel time prediction task. Extensive experiments on two real-world datasets show that the MTTPRE outperforms all the competitors with significant improvement and remarkable robustness.

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Cited By

Gou XHu LWang DZhang X(2023)A Fundamental Model with Stable Interpretability for Traffic ForecastingProceedings of the 1st ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies10.1145/3615889.3628510(10-13)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615889.3628510
Zhao XWang SWang HHe TZhang DWang GFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)HST-GT: Heterogeneous Spatial-Temporal Graph Transformer for Delivery Time Estimation in Warehouse-Distribution Integration E-CommerceProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614918(3402-3411)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614918

Index Terms

MTTPRE: a multi-scale spatial-temporal model for travel time prediction
1. Applied computing
  1. Operations research
    1. Transportation
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems

November 2022

806 pages

ISBN:9781450395298

DOI:10.1145/3557915

General Chairs:
Matthias Renz
Kiel University, Germany
,
Mohamed Sarwat
Wherobots Inc. / Arizona State University

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Published: 22 November 2022

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SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems

November 1 - 4, 2022

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Cited By

Gou XHu LWang DZhang X(2023)A Fundamental Model with Stable Interpretability for Traffic ForecastingProceedings of the 1st ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies10.1145/3615889.3628510(10-13)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615889.3628510
Zhao XWang SWang HHe TZhang DWang GFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)HST-GT: Heterogeneous Spatial-Temporal Graph Transformer for Delivery Time Estimation in Warehouse-Distribution Integration E-CommerceProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614918(3402-3411)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614918

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