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MTLM: a multi-task learning model for travel time estimation

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Abstract

Travel time estimation (TTE) is an important research topic in many geographic applications for smart city research. However, existing approaches either ignore the impact of transportation modes, or assume the mode information is known for each training trajectory and the query input. In this paper, we propose a multi-task learning model for travel time estimation called MTLM, which recommends the appropriate transportation mode for users, and then estimates the related travel time of the path. It integrates transportation-mode recommendation task and travel time estimation task to capture the mutual influence between them for more accurate TTE results. Furthermore, it captures spatio-temporal dependencies and transportation mode effect by learning effective representations for TTE. It combines the transportation-mode recommendation loss and TTE loss for training. Extensive experiments on real datasets demonstrate the effectiveness of our proposed methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61872258, 61772356, 61876117 and 61802273, the Open Program of State Key Laboratory of Software Architecture under item number SKLSAOP1801, Dongguan Innovative Research Team Program (No.2018607201008), and Blockshine corporation.

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    Authors and Affiliations

    1. Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Suzhou, China

      Saijun Xu & Jiajie Xu

    2. School of Computer Science and Technology, Soochow University, Suzhou, China

      Ruoqian Zhang

    3. Neusoft Corporation, Shenyang, China

      Wanjun Cheng

    4. Institute of Electronic and Information Engineering of UESTC, Dongguan, Guangdong, China

      Jiajie Xu

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    1. Saijun Xu
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    2. Ruoqian Zhang
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    Correspondence to Jiajie Xu.

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    Saijun Xu and Ruoqian Zhang are equally contributed co-first authors.

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    Xu, S., Zhang, R., Cheng, W. et al. MTLM: a multi-task learning model for travel time estimation. Geoinformatica 26, 379–395 (2022). https://doi.org/10.1007/s10707-020-00422-x

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