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Media type:
E-Article
Title:
Route Travel Time Estimation on a Road Network Revisited : Heterogeneity, Proximity, Periodicity and Dynamicity
:
Heterogeneity, Proximity, Periodicity and Dynamicity
Published:
Association for Computing Machinery (ACM), 2022
Published in:
Proceedings of the VLDB Endowment, 16 (2022) 3, Seite 393-405
Language:
English
DOI:
10.14778/3570690.3570691
ISSN:
2150-8097
Origination:
Footnote:
Description:
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.