Complementary Fusion of Deep Network and Tree Model for ETA Prediction

Huang, YuRui; Zhang, Jie; Bao, Hengda; Yang, Yang; Yang, Jian

doi:10.1145/3474717.3488237

Proceedings of the 29th International Conference on Advances in Geographic Information Systems 2021

DOI: 10.1145/3474717.3488237

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Complementary Fusion of Deep Network and Tree Model for ETA Prediction

YuRui Huang

Jie Zhang²,

Hengda Bao

et al.

Abstract: Estimated time of arrival (ETA) is a very important factor in the transportation system. It has attracted increasing attentions and has been widely used as a basic service in navigation systems and intelligent transportation systems. In this paper, we propose a novel solution to the ETA estimation problem, which is an ensemble on tree models and neural networks. We proved the accuracy and robustness of the solution on the A/B list and finally won first place in the SIGSPATIAL 2021 GISCUP competition.

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Route Travel Time Estimation on a Road Network Revisited

Yuan

Bao

2022

Proc. VLDB Endow.

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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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Route Travel Time Estimation on a Road Network Revisited

Yuan

Bao

2022

Proc. VLDB Endow.

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show abstract

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Complementary Fusion of Deep Network and Tree Model for ETA Prediction

Cited by 1 publication

References 11 publications

Route Travel Time Estimation on a Road Network Revisited

Route Travel Time Estimation on a Road Network Revisited

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