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• W4285282135 abstract "The estimated time of arrival (ETA) plays a vital role in intelligent transportation systems and has been widely used as a basic service in ride-hailing platforms. Obtaining a precise ETA is a challenging task due to the complexity of the real-world geographic and traffic environments. Previous works suffer from heterogeneous sparse data learning and multiple-correlation extraction issues. Therefore, this paper presents a hybrid deep learning framework (HSETA) to estimate the vehicle travel time from massive data. First, we encode heterogeneous data to represent various features in different respects. Then, we develop an ensemble factorization machine block (EFMB) structure combined with gated recurrent unit (GRU) and multilayer perceptron (MLP) to extract information from sparse and dense features. Next, the multiple-correlation learning block (MCLB) structure that we propose is utilized to aggregate information based on multiple correlations. Finally, the travel time can be estimated by simple regression. Our extensive evaluations on two real-world datasets show that HSETA significantly outperforms all baselines. Our PyTorch implementation of HSETA and sample data are available at <uri xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>https://github.com/LouisChenki/HSETA</uri>" @default.
• W4285282135 created "2022-07-14" @default.
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• W4285282135 date "2022-11-01" @default.
• W4285282135 modified "2023-10-16" @default.
• W4285282135 title "HSETA: A Heterogeneous and Sparse Data Learning Hybrid Framework for Estimating Time of Arrival" @default.
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• W4285282135 doi "https://doi.org/10.1109/tits.2022.3170917" @default.
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