| Title | Travel Time Estimation Based on Neural Network with Auxiliary Loss |
| Creator | |
| Date Issued | 2021-11-02 |
| Source Publication | GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
 |
| Pages | 642-645 |
| Abstract | Estimated Time of Arrival (ETA) plays an important role in various applications, for instance, scene of order dispatch, estimate price, travel time prediction, route decision, etc. In this project, we propose a new systematical Wide-Deep-Double-Recurrent model with Auxiliary loss (WDDRA), which involves Auxiliary Loss for Link Current Status prediction task. Our extensive evaluations show that WDDRA significantly outperforms the state-of-the-art learning algorithms. And our final ensemble model wins second place on the SIGSPATIAL 2021 GISCUP leaderboard without data augmentation. Our source code is available at:https://github.com/Phimos/SIGSPATIAL-2021-GISCUP-2nd-Place-Solution |
| Keyword | deep neural network ensemble learning estimated time of arrival link current state |
| DOI | 10.1145/3474717.3488238 |
| URL | View source |
| Language | 英语English |
| Scopus ID | 2-s2.0-85119203800 |
| Citation statistics |
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| Document Type | Conference paper |
| Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/8304 |
| Collection | Beijing Normal-Hong Kong Baptist University |
| Affiliation | 1.Peking University,Beijing,China 2.Beijing Normal University,Hong Kong Baptist University United International College,Zhuhai,China |
| Recommended Citation GB/T 7714 | Gan，Yunchong,Zhang，Haoyu,Wang，Mingjie. Travel Time Estimation Based on Neural Network with Auxiliary Loss[C], 2021: 642-645. |
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