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

Xu, Saijun ; Zhang, Ruoqian ; Cheng, Wanjun ; Xu, Jiajie

GeoInformatica, 2022-04, Vol.26 (2), p.379-395 [Periódico revisado por pares]

New York: Springer US

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Título:
MTLM: a multi-task learning model for travel time estimation
Autor: Xu, Saijun ; Zhang, Ruoqian ; Cheng, Wanjun ; Xu, Jiajie
Assuntos: Computer Science ; Data Structures and Information Theory ; Deep learning ; Estimates ; Geographical Information Systems/Cartography ; Holidays & special occasions ; Information Storage and Retrieval ; Learning ; Multimedia Information Systems ; Smart cities ; Traffic ; Training ; Transportation ; Travel ; Travel time
É parte de: GeoInformatica, 2022-04, Vol.26 (2), p.379-395
Descrição: 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.
Editor: New York: Springer US
Idioma: Inglês

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

Xu, Saijun ; Zhang, Ruoqian ; Cheng, Wanjun ; Xu, Jiajie

New York: Springer US

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