Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit

被引:19
作者
Fu, Xin [1 ,2 ]
Luo, Wei [1 ]
Xu, Chengyao [1 ]
Zhao, Xiaoxuan [1 ]
机构
[1] Changan Univ, Coll Transportat Engn, Xian 710064, Peoples R China
[2] Minist Educ, Engn Res Ctr Highway Infrastruct Digitalizat, Xian 710064, Shaanxi, Peoples R China
关键词
NEAREST NEIGHBOR MODEL; NEURAL-NETWORK;
D O I
10.1155/2020/3697625
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed prediction model based on wavelet transform and neural network is, therefore, proposed in this article to improve traffic prediction methods. The wavelet transform is used to decompose the original traffic speed data, and then the coefficients obtained after the decomposition are used to reconstruct the high-frequency random sequences and the low-frequency trend sequence. Secondly, a GRU neural network is constructed to learn the trend of low-frequency sequence. The spatiotemporal correlation between input data is extracted by adjusting the input of the model. Meanwhile, an ARMA model is used to fit unstable random fluctuations of high-frequency sequences. Last of all, the prediction results of the two models are added together to obtain the final prediction result. The proposed prediction model is validated by using road section speed data based on the floating car data collected in Ningbo. The results show that the proposed model has high accuracy and robustness.
引用
收藏
页数:13
相关论文
共 45 条
[1]  
[Anonymous], 2014, C EMPIRICAL METHODS, DOI 10.3115/v1/d14-1179.
[2]  
[Anonymous], 2018, P 27 INT JOINT C ART
[3]   Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction [J].
Asif, Muhammad Tayyab ;
Dauwels, Justin ;
Goh, Chong Yang ;
Oran, Ali ;
Fathi, Esmail ;
Xu, Muye ;
Dhanya, Menoth Mohan ;
Mitrovic, Nikola ;
Jaillet, Patrick .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2014, 15 (02) :794-804
[4]   A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting [J].
Cai, Pinlong ;
Wang, Yunpeng ;
Lu, Guangquan ;
Chen, Peng ;
Ding, Chuan ;
Sun, Jianping .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2016, 62 :21-34
[5]  
Chai YC, 2016, CHIN CONT DECIS CONF, P7030, DOI 10.1109/CCDC.2016.7532264
[6]  
Chang G, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING (ICITE), P8, DOI 10.1109/ICITE.2016.7581298
[7]   Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on SpaceTime Analysis and GRU [J].
Dai, Guowen ;
Ma, Changxi ;
Xu, Xuecai .
IEEE ACCESS, 2019, 7 :143025-143035
[8]   Flexible Certificate Revocation List for Efficient Authentication in IoT [J].
Duan, Li ;
Li, Yong ;
Liao, Lijun .
PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON THE INTERNET OF THINGS (IOT'18), 2018,
[9]   Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data [J].
Duan, Zongtao ;
Yang, Yun ;
Zhang, Kai ;
Ni, Yuanyuan ;
Bajgain, Saurab .
IEEE ACCESS, 2018, 6 :31820-31827
[10]   Short-term speed predictions exploiting big data on large urban road networks [J].
Fusco, Gaetano ;
Colombaroni, Chiara ;
Isaenko, Natalia .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2016, 73 :183-201