research-article

Route Travel Time Estimation on a Road Network Revisited: Heterogeneity, Proximity, Periodicity and Dynamicity

Authors:

Zhifeng BaoAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 16, Issue 3

Pages 393 - 405

https://doi.org/10.14778/3570690.3570691

Published: 01 November 2022 Publication History

Abstract

In this paper, we revisit the problem of route travel time estimation on a road network and aim to boost its accuracy by capturing and utilizing spatio-temporal features from four significant aspects: heterogeneity, proximity, periodicity and dynamicity.

Spatial-wise, we consider two forms of heterogeneity at link level in a road network: the turning ways between different links are heterogeneous which can make the travel time of the same link various; different links contain heterogeneous attributes and thereby lead to different travel time. In addition, we take into account the proximity: neighboring links have similar traffic patterns and lead to similar travel speeds. To this end, we build a link-connection graph to capture such heterogeneity and proximity.

Temporal-wise, the weekly/daily periodicity of temporal background information (e.g., rush hours) and dynamic traffic conditions have significant impact on the travel time, which result in static and dynamic spatio-temporal features respectively. To capture such impacts, we regard the travel time/speed as a combination of static and dynamic parts, and extract many spatio-temporal relevant features for the prediction task.

Talking about the methodology, it remains an open problem to build a generic learning model to boost the estimation accuracy. Hence, we design a novel encoder-decoder framework - The encoder uses the sequence attention model to encode dynamic features from the temporal-wise perspective. The decoder first uses the heterogeneous graph attention model to decode the static part of travel speed based on static spatio-temporal features, and then leverages the sequence attention model to decode the estimated travel time from spatial-wise perspective. Extensive experiments on real datasets verify the superiority of our method as well as the importance of the four aspects outlined above.

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Cited By

Yuan HCong GLi G(2024)Nuhuo: An Effective Estimation Model for Traffic Speed Histogram Imputation on A Road NetworkProceedings of the VLDB Endowment10.14778/3654621.365462817:7(1605-1617)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.14778/3654621.3654628
Yuan HWang SBao ZWang S(2023)Automatic Road Extraction with Multi-Source Data Revisited: Completeness, Smoothness and DiscriminationProceedings of the VLDB Endowment10.14778/3611479.361150416:11(3004-3017)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611504
Yi PHuang FWang JPeng J(2023)Topology augmented dynamic spatial-temporal network for passenger flow forecasting in urban rail transitApplied Intelligence10.1007/s10489-023-04651-z53:21(24655-24670)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s10489-023-04651-z

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 16, Issue 3

November 2022

181 pages

ISSN:2150-8097

Editors:
Georgia Koutrika
Athena Research Center
,
Jun Yang
Duke University

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VLDB Endowment

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Published: 01 November 2022

Published in PVLDB Volume 16, Issue 3

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Cited By

Yuan HCong GLi G(2024)Nuhuo: An Effective Estimation Model for Traffic Speed Histogram Imputation on A Road NetworkProceedings of the VLDB Endowment10.14778/3654621.365462817:7(1605-1617)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.14778/3654621.3654628
Yuan HWang SBao ZWang S(2023)Automatic Road Extraction with Multi-Source Data Revisited: Completeness, Smoothness and DiscriminationProceedings of the VLDB Endowment10.14778/3611479.361150416:11(3004-3017)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611504
Yi PHuang FWang JPeng J(2023)Topology augmented dynamic spatial-temporal network for passenger flow forecasting in urban rail transitApplied Intelligence10.1007/s10489-023-04651-z53:21(24655-24670)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s10489-023-04651-z

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