A dynamic ensemble deep deterministic policy gradient recursive network for spatiotemporal traffic speed forecasting in an urban road network
文献类型：期刊论文
| 作者 | Mi, Xiwei1,5; Yu, Chengqing4; Liu, Xinwei2; Yan, Guangxi3; Yu, Fuhao5; Shang, Pan5 |
| 刊名 | DIGITAL SIGNAL PROCESSING
 |
| 出版日期 | 2022-09-01 |
| 卷号 | 129页码:16 |
| 关键词 | Spatiotemporal traffic speed forecasting Deep deterministic policy gradient Simple recursive network Temporal convolution network |
| ISSN号 | 1051-2004 |
| DOI | 10.1016/j.dsp.2022.103643 |
| 英文摘要 | Traffic congestion is a difficult problem that restricts the construction of urbanization. Spatiotemporal traffic speed forecasting technologies can provide effective technical support for alleviating traffic congestion and ensuring vehicle travel safety. The ensemble learning algorithm is a hot topic in traffic speed modeling. In this field, previous ensemble learning methods mainly adopt the principle of static modeling, which limits the learning ability of the model to dynamic features. To solve this problem, in this paper, a new dynamic ensemble deep deterministic policy gradient recursive network is presented for traffic speed forecasting, which comprises three main modeling steps. In step I, the simple recursive network (SRU) and temporal convolution network (TCN) methods are used as the main predictors to build the traffic speed forecasting model. In step II, the multi-objective imperialist competitive algorithm (MOICA) integrates these neural networks by optimizing the weight coefficients and generating the Pareto solution set. In step III, the deep deterministic policy gradient (DDPG) method dynamically selects the Pareto optimal solution of the MOICA according to the changes in the traffic speed data. The MOICA and DDPG dynamically integrate the forecasting results from the SRU and TCN to obtain the final results. Based on the experimental analysis results, several conclusions can be given as follows: (a) the model presented in this paper can obtain accurate traffic speed forecasting results with MAPE values below 4% on all data sets. (b) the proposed model can achieve better results than thirteen alternative models and four proposed models from other researchers. (c) the proposed model can improve the prediction performance of traditional predictors by about 6%. (C) 2022 Elsevier Inc. All rights reserved. |
| 资助项目 | National Natural Science Foundation of China[52102471] ; National Natural Science Foundation of China[72001020] ; Beijing Natural Science Foundation[L201016] ; Fundamental Research Funds for the Central Universities (Science and technology leading talent team project)[2022JBXT008] ; National Key Research and Development Program of China[2018YFB1201402] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000862260300014 |
| 出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19810]  |
| 专题 | 中国科学院计算技术研究所期刊论文 |
| 通讯作者 | Shang, Pan |
| 作者单位 | 1.Beijing Jiaotong Univ, Collaborat Innovat Ctr Railway Traff Safety, Beijing 10004, Peoples R China 2.Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing 10004, Peoples R China 3.Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China |
| 推荐引用方式 GB/T 7714 | Mi, Xiwei,Yu, Chengqing,Liu, Xinwei,et al. A dynamic ensemble deep deterministic policy gradient recursive network for spatiotemporal traffic speed forecasting in an urban road network[J]. DIGITAL SIGNAL PROCESSING,2022,129:16. |
| APA | Mi, Xiwei,Yu, Chengqing,Liu, Xinwei,Yan, Guangxi,Yu, Fuhao,&Shang, Pan.(2022).A dynamic ensemble deep deterministic policy gradient recursive network for spatiotemporal traffic speed forecasting in an urban road network.DIGITAL SIGNAL PROCESSING,129,16. |
| MLA | Mi, Xiwei,et al."A dynamic ensemble deep deterministic policy gradient recursive network for spatiotemporal traffic speed forecasting in an urban road network".DIGITAL SIGNAL PROCESSING 129(2022):16. |
入库方式： OAI收割
来源：计算技术研究所
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