Knowledge Commons of Institute of Automation,CAS
Differential Time-variant Traffic Flow Prediction Based on Deep Learning | |
Wei, Zhang1,2; Fenghua, Zhu1; Yuanyuan, Chen1; Xiao, Wang1; Gang, Xiong1; Fei-Yue, Wang1 | |
2020 | |
会议名称 | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems |
页码 | 1-6 |
会议日期 | 20-23 Sept. 2020 |
会议地点 | Rhodes, Greece |
出版者 | IEEE |
摘要 | The accuracy of traffic flow prediction significantly impacts the operation of Intelligent Transportation Systems (ITS). In this paper, we propose a Differential Time-variant (DT) Traffic Flow Prediction method, which can remarkably improve the accuracy and reduce the variance of traffic flow forecast based on deep learning models. To extract the temporal trend of the traffic flow at different locations, we apply data difference to preprocess the raw traffic data. This method can better eliminate the uncertainties of traffic flow series like volatility and anomaly. Then, time information is introduced in the form of One-Hot Encoding to effectively model the temporal patterns of traffic flow. Necessary analysis is presented to demonstrate the rationality. Three popular deep neural networks are applied to test our method, and experimental results on PeMS data sets indicate that it can make more accurate prediction compared with the same model. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44312 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Yuanyuan, Chen |
作者单位 | 1.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wei, Zhang,Fenghua, Zhu,Yuanyuan, Chen,et al. Differential Time-variant Traffic Flow Prediction Based on Deep Learning[C]:IEEE,2020:1-6. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
09294745.pdf(795KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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