Knowledge Commons of Institute of Automation,CAS
Short-term Traffic Flow Prediction with LSTM Recurrent Neural Network | |
Kang, Danqing; Lv, Yisheng; Chen, Yuanyuan | |
2017 | |
会议名称 | IEEE International Conference on Intelligent Transportation Systems |
会议日期 | 2017 |
会议地点 | Japan |
摘要 | Accurate and timely short-term traffic flow prediction plays an important role in intelligent transportation management and control. Traffic flow prediction has a long history and is still a difficult problem due to intrinsically highly nonlinear and stochastic characteristics of complex transportation systems. In this paper, we employ the long short-term memory (LSTM) recurrent neural network to analyze the effects of various input settings on the LSTM prediction performances. Flow, speed, and occupancy at the same detector station are used as inputs to predict traffic flow. The results show that the inclusion of occupancy/speed information may help to enhance the performance of the model overall. Further, we include as inputs traffic variables from the upstream and/or downstream detector stations for traffic flow prediction. The evaluation of such spatial-temporal input interactions show that the inclusion of both downstream and upstream traffic information is useful in improving prediction accuracy. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/20175 |
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 | Lv, Yisheng |
作者单位 | State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, 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 | Kang, Danqing,Lv, Yisheng,Chen, Yuanyuan. Short-term Traffic Flow Prediction with LSTM Recurrent Neural Network[C],2017. |
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2017Kang.pdf(246KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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