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Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information
| 题名： | Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information |
| 正文语种： | eng |
| 作者： | Tang, Kun;Chen, Shuyan;Khattak, Aemal J.;Pan, Yingjiu |
| 作者单位： | Southeast Univ Jiangsu Key Lab Urban ITS Nanjing Jiangsu Peoples R China|Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban Nanjing Jiangsu Peoples R China|Southeast Univ Sch Transportat Sipailou 2 Nanjing Jiangsu Peoples R China |
| 关键词： | Contextual features;data-driven;deep learning;sparse denoising auto-encoder;urban road network |
| 摘要： | To meet the growing demand of accurate and reliable travel time information in intelligent transportation systems, this a develops a deep architecture incorporating contextual information to estimate travel time in urban road network from a citywide persp |
| 出版年： | 2021 |
| 期刊名称： | Journal of Intelligent Transportation Systems |
| 卷： | 25 |
| 期： | 1/6 |
| 页码： | 313-329 |
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