MTLM: a multi-task learning model for travel time estimation
| Autor: | Saijun Xu, Jiajie Xu, Wanjun Cheng, Ruoqian Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Estimation
Computer science business.industry Geography Planning and Development Mode (statistics) Multi-task learning 02 engineering and technology Machine learning computer.software_genre Task (project management) 020204 information systems Smart city Path (graph theory) 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Mode effect Artificial intelligence business computer Information Systems |
| Zdroj: | GeoInformatica. 26:379-395 |
| ISSN: | 1573-7624 1384-6175 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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