Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network
文献类型：期刊论文
| 作者 | Pang, Junbiao1; Huang, Jing7; Du, Yong6; Yu, Haitao6; Huang, Qingming4,5; Yin, Baocai2,3 |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2019-09-01 |
| 卷号 | 20期号:9页码:3283-3293 |
| 关键词 | Bus arriving time prediction recurrent neural network heterogenous measurement long-range dependencies multi-step-ahead prediction |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2018.2873747 |
| 英文摘要 | Bus arrival time prediction intends to improve the level of the services provided by transportation agencies. Intuitively, many stochastic factors affect the predictability of the arrival time, e.g., weather and local events. Moreover, the arrival time prediction for a current station is closely correlated with that of multiple passed stations. Motivated by the observations above, this paper proposes to exploit the long-range dependencies among the multiple time steps for bus arrival prediction via recurrent neural network (RNN). Concretely, RNN with long short-term memory block is used to "correct" the prediction for a station by the correlated multiple passed stations. During the correlation among multiple stations, one-hot coding is introduced to fuse heterogeneous information into a unified vector space. Therefore, the proposed framework leverages the dynamic measurements (i.e., historical trajectory data) and the static observations (i.e., statistics of the infrastructure) for bus arrival time prediction. In order to fairly compare with the state-of-the-art methods, to the best of our knowledge, we have released the largest data set for this task. The experimental results demonstrate the superior performances of our approach on this data set. |
| 资助项目 | Natural Science Foundation of China[61672069] ; Natural Science Foundation of China[61872333] ; Natural Science Foundation of China[61620106009] ; China Post-Doctoral Research Foundation ; Beijing Municipal Commission of Education[KM201610005034] ; Beijing Municipal Commission of Transport Science and Technology Project |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000484207200008 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/4762]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Pang, Junbiao; Yu, Haitao |
| 作者单位 | 1.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China 2.Beijing Univ Technol, Beijing 100124, Peoples R China 3.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China 4.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 6.Beijing Transportat Informat Ctr, Beijing 100161, Peoples R China 7.IBM China Investment Co Ltd, Beijing 10085, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pang, Junbiao,Huang, Jing,Du, Yong,et al. Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2019,20(9):3283-3293. |
| APA | Pang, Junbiao,Huang, Jing,Du, Yong,Yu, Haitao,Huang, Qingming,&Yin, Baocai.(2019).Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,20(9),3283-3293. |
| MLA | Pang, Junbiao,et al."Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 20.9(2019):3283-3293. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。