A novel hybrid model combining a fuzzy inference system and a deep learning method for short-term traffic flow prediction

被引:23
作者
Liu, Yan [1 ]
Wang, Xiao-kang [1 ]
Hou, Wen-hui [1 ]
Liu, Hui [1 ]
Wang, Jian-qiang [1 ,2 ,3 ]
机构
[1] Cent South Univ, Sch Business, Changsha 410083, Peoples R China
[2] Hunan Engn Res Ctr Intelligent Decis Making & Big, Xiangtan 411201, Peoples R China
[3] 932 South Lushan Rd, Changsha 410083, Hunan, Peoples R China
关键词
Traffic flow prediction; GRU neural network; Fuzzy inference system; Temporal feature enhancement; NEURAL-NETWORK; LSTM; SVR; IDENTIFICATION; CNN;
D O I
10.1016/j.knosys.2022.109760
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Deep learning techniques have been widely used in traffic flow prediction. They can perform much better than shallow models. However, most existing deep learning models only focus on deterministic data, ignoring the fact that traffic flow contains a large amount of uncertain data. Therefore, this paper proposes a novel hybrid model called FGRU combining a fuzzy inference system (FIS) and a gated recurrent unit (GRU) neural network to predict short-term traffic flows. The GRU model is applied to capture the temporal dependencies within traffic flow data, and the FIS makes up for the shortcomings of deep learning by lessening the influence of uncertain data. In addition, a temporal feature enhancement mechanism is proposed to calculate the appropriate time intervals as model inputs. The most appropriate model structure and parameters are explored by performing comparative experiments. Finally, the simulation results show that the mean absolute error of FGRU is 7.75% and 3.05% lower than ARIMA and the state-of-the-art traffic flow prediction model based on deep learning.(c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:13
相关论文
共 50 条
[41]   SSA-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting [J].
Wang, Fei ;
Liang, Yinxi ;
Lin, Zhizhe ;
Zhou, Jinglin ;
Zhou, Teng .
MATHEMATICS, 2024, 12 (12)
[42]   Short-term traffic flow prediction based on ACBiGRU model [J].
Zhang X. ;
Zhang G. ;
Zhang H. ;
Zhang X. .
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2023, 51 (05) :88-93
[43]   Short-term traffic flow prediction based on a hybrid optimization algorithm [J].
Yan, He ;
Zhang, Tian'an ;
Qi, Yong ;
Yu, Dong-Jun .
APPLIED MATHEMATICAL MODELLING, 2022, 102 :385-404
[44]   A Piecewise Hybrid of ARIMA and SVMs for Short-Term Traffic Flow Prediction [J].
Wang, Yong ;
Li, Li ;
Xu, Xiaofei .
NEURAL INFORMATION PROCESSING, ICONIP 2017, PT V, 2017, 10638 :493-502
[45]   Short-Term Traffic Congestion Prediction with Deep Learning for LoRa Networks [J].
Salahdine, Fatima ;
Aggarwal, Shobhit ;
Nasipuri, Asis .
SOUTHEASTCON 2022, 2022, :261-268
[46]   A hybrid deep learning based traffic flow prediction method and its understanding [J].
Wu, Yuankai ;
Tan, Huachun ;
Qin, Lingqiao ;
Ran, Bin ;
Jiang, Zhuxi .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2018, 90 :166-180
[47]   A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine [J].
Shang, Qiang ;
Lin, Ciyun ;
Yang, Zhaosheng ;
Bing, Qichun ;
Zhou, Xiyang .
PLOS ONE, 2016, 11 (08)
[48]   A Hybrid Model based on Deep Learning and Cross-attention for Short-term Wind Power Prediction [J].
Zhang, Yiqin ;
Peng, Cheng .
2022 5TH INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY AND POWER ENGINEERING, REPE, 2022, :351-355
[49]   Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks [J].
Hu, Guojing ;
Whalin, Robert W. ;
Kwembe, Tor A. ;
Lu, Weike .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2023, 632
[50]   Hybrid Extreme Learning for Reliable Short-Term Traffic Flow Forecasting [J].
Chen, Huayuan ;
Lin, Zhizhe ;
Yao, Yamin ;
Xie, Hai ;
Song, Youyi ;
Zhou, Teng .
MATHEMATICS, 2024, 12 (20)