Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data
文献类型：期刊论文
| 作者 | Zhao, Jiandong3,4; Gao, Yuan2; Bai, Zhiming2; Lu, Shuhan2; Wang, Hao1 |
| 刊名 | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
 |
| 出版日期 | 2019-06-01 |
| 卷号 | 11期号:2页码:70-81 |
| ISSN号 | 1939-1390 |
| DOI | 10.1109/MITS.2019.2903431 |
| 英文摘要 | The full utilization of Location-Based Vehicle Sensor Data (LB-VSD) can improve the efficiency of traffic control and management. Currently, LB-VSD is widely applied to the prediction of traffic speed. Like the GPS system, BeiDou satellite navigation system (BDS) can collect LB-VSD. In China, the key operation vehicles on the expressway are equipped with BDS to monitor the travel path. This provides a basis for predicting the traffic speed on expressway accurately. In this paper, considering the abnormal data collected by BDS, the screening and processing rules are made, and then the traffic speed sequence is extracted. Considering the data-missing problem caused by equipment failure or abnormal data elimination and the data sparse problem caused by small size of sample, a filling method based on trend-historical data is proposed. Traffic flow evolution is a complex process. Sudden accidents or bad weather can cause a sudden change in traffic flow and non-recurrent traffic congestion. The prediction accuracy of traditional machine learning methods is low when non-recurrent congestion occurred. In order to solve this problem, this paper adopts a deep learning model-Long Short-Term Memory (LSTM) to predict the traffic speed. Moreover, three-regime algorithm is used while building the prediction model. The prediction method is compared with Support Vector Regression (SVR) method. The results show that the prediction accuracy of the proposed method is higher than that of SVR algorithm, and the robustness is better in the case of non-recurrent traffic congestion. |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000466040900009 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/4227]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhao, Jiandong |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Jining Branch, Jining, Peoples R China 2.Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing, Peoples R China 3.Beijing Jiaotong Univ, Key Lab Urban Transportat Complex Syst Theory & T, Minist Educ, Beijing, Peoples R China 4.Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Jiandong,Gao, Yuan,Bai, Zhiming,et al. Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data[J]. IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,2019,11(2):70-81. |
| APA | Zhao, Jiandong,Gao, Yuan,Bai, Zhiming,Lu, Shuhan,&Wang, Hao.(2019).Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data.IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,11(2),70-81. |
| MLA | Zhao, Jiandong,et al."Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data".IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 11.2(2019):70-81. |
入库方式： OAI收割
来源：计算技术研究所
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