Please wait a minute...
Chin. Phys. B, 2022, Vol. 31(4): 040502    DOI: 10.1088/1674-1056/ac3647
GENERAL Prev   Next  

Traffic flow prediction based on BILSTM model and data denoising scheme

Zhong-Yu Li(李中昱)1,2,3, Hong-Xia Ge(葛红霞)1,2,3,†, and Rong-Jun Cheng(程荣军)1,2,3
1 Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China;
2 Jiangsu Provincial Collaborative Innovation Center for Modern Urban Traffic Technologies, Nanjing 210096, China;
3 National Traffic Management Engineering and Technology Research Center Ningbo University Subcenter, Ningbo 315211, China
Abstract  Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems. Accurate prediction can alleviate traffic congestion, and reduce environmental pollution. For the management department, it can make effective use of road resources. For individuals, it can help people plan their own travel paths, avoid congestion, and save time. Owing to complex factors on the road, such as damage to the detector and disturbances from environment, the measured traffic volume can contain noise. Reducing the influence of noise on traffic flow prediction is a piece of very important work. Therefore, in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction. At the same time, three denoising algorithms are compared to find the best combination mode. In this paper, the wavelet (WL) denoising scheme, the empirical mode decomposition (EMD) denoising scheme, and the ensemble empirical mode decomposition (EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data. In addition, we combine the denoising schemes with bidirectional long short-term memory (BILSTM) network to predict the traffic flow. The data in this paper are cited from performance measurement system (PeMS). We choose three kinds of road data (mainline, off ramp, on ramp) to predict traffic flow. The results for mainline show that data denoising can improve prediction accuracy. Moreover, prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods (BILSTM+WL, BILSTM+EMD, BILSTM+EEMD). The results for off ramp and on ramp show the same performance as the results for mainline. It is indicated that this model is suitable for different road sections and long-term prediction.
Keywords:  traffic flow prediction      bidirectional long short-term memory network      data denoising  
Received:  13 July 2021      Revised:  12 October 2021      Accepted manuscript online:  04 November 2021
PACS:  05.60.-k (Transport processes)  
  45.70.Vn (Granular models of complex systems; traffic flow)  
  02.30.Oz (Bifurcation theory)  
Fund: Project supported by the Program of Humanities and Social Science of the Education Ministry of China (Grant No. 20YJA630008), the Natural Science Foundation of Zhejiang Province, China (Grant No. LY20G010004), and the K C Wong Magna Fund in Ningbo University, China.
Corresponding Authors:  Hong-Xia Ge     E-mail:

Cite this article: 

Zhong-Yu Li(李中昱), Hong-Xia Ge(葛红霞), and Rong-Jun Cheng(程荣军) Traffic flow prediction based on BILSTM model and data denoising scheme 2022 Chin. Phys. B 31 040502

[1] Do L N N, Vu H L, Vo B Q, Liu Z Y and Phung D 2019 Transportation Research Part C:Emerging Technologies 108 12
[2] Wu Y, Tan H, Qin L, Ran B and Jiang Z X 2018 Transportation Research Part C:Emerging Technologies 90 166
[3] Williams B M 2001 Transportation Research Record 1776 194
[4] Chen C Y, Hu J M, Meng Q and Zhang Y 2011 2011 IEEE Intelligent Vehicles Symposium (IV) pp. 607-612
[5] Kumar S V and Vanajakshi L 2015 European Transport Research Review 7 1
[6] Giraka O and Selvaraj V K 2020 Transportation Letters 12 483
[7] Chen R, Liang C Y, Hong W C and Gu D X 2015 Applied Soft Computing 26 435
[8] Zhang L, Alharbe N R, Luo G C, Yao Z Y and Li Y 2018 Tsinghua Science and Technology 23 479
[9] Cheng A Y, Jiang X, Li Y F, Zhang C and Zhu H 2017 Physica A 466 422
[10] Li C, Wang X D, Cheng Z W and Bai Y 2020 IEEE Access 8 19717
[11] More R, Mugal A, Rajgure S, Adhao R B and Pachghare V K 2016 2016 International Conference on Computing, Analytics and Security Trends (CAST), pp. 52-57
[12] Kumar K, Parida M and Katiyar V K 2015 Transport 30 397
[13] Zhang W B, Yu Y H, Qi Y, Shu F and Wang Y H 2019 Transportmetrica A:Transport Science 15 1688
[14] Yu F, Wei D, Zhang S T and Shao Y L 2019 2019 4th International Conference on Intelligent Transportation Engineering (ICITE), pp. 99-103
[15] An J Y, Fu L, Hu M, Chen W H and Zhan J W 2019 IEEE Access 7 20708
[16] Sha S W, Li J, Zhang K, Yang Z F, Wei Z J, Li X Y and Zhu X 2020 IEEE Access 8 15232
[17] Duan Y J, Yisheng L V and Wang F Y 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), pp. 1053-1058
[18] Wu Y K, Tan H C, Qin L Q, Ran B and Jiang Z X 2018 Transportation Research Part C:Emerging Technologies 90 166
[19] Hou Q Z, Leng J Q, Ma G S, Liu W Y and Cheng Y X 2019 Physica A 527 121065
[20] Tang J J, Zhang G H, Wang Y H, Wang H and Liu Fang 2015 Transportation Research Part C:Emerging Technologies 51 29
[21] Zheng H F, Lin F, Feng X X and Chen Y J 2020 IEEE Transactions on Intelligent Transportation Systems 22 6910
[22] Chen X Q, Li Z B, Yang Y S, Qi L and Ke R 2020 IEEE Transactions on Intelligent Transportation Systems 22 3190
[23] Chen X Y, He Z C and Sun L J 2019 Transportation research part C:emerging technologies 98 73
[24] Zheng T X, Girgis A A and Makram E B 2000 Electric Power Systems Research 54 11
[25] Xie Y C, Zhang Y L and Ye Z R 2007 Computer-Aided Civil and Infrastructure Engineering 22 326
[26] Dunne S and Ghosh B 2013 IEEE Transactions on Intelligent Transportation Systems 14 370
[27] Xiao H, Sun H Y, Ran B and Oh Y 2003 Transportation Research Record:Journal of the Transportation Research Board 1836 16
[28] Yang W, Yang D Y, Zhao Y L and Gong J L 2010 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM). 2 pp. 969-972
[29] Wang H Z, Liu L, Dong S J, Qian Z and Wei H 2016 Transportmetrica B:Transport Dynamics 4 159
[30] Chen X Q, Chen H X, Yang Y S, Wu H F, Zhang W H, Zhao J S and Xiong Y 2021 Physica A 565 125574
[31] Tang J J, Gao F, Liu F and Chen X Q 2020 IEEE Access 8 11546
[32] Chen X Q, Lu J Q, Zhao J S, Qu Z J, Yang Y S and Xian J F 2020 Sustainability 12 3678
[33] Huang D W 2003 Physica A:Statistical Mechanics and its Applications 329 298
[34] Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q A, Yen N C, Tung C C and Liu H H 1998 Proc. Royal Soc. London. Series A 454 903
[35] Hochreiter S and Schmidhuber J 1997 Neural Computation 9 1735
[1] An extended smart driver model considering electronic throttle angle changes with memory
Congzhi Wu(武聪智), Hongxia Ge(葛红霞), and Rongjun Cheng(程荣军). Chin. Phys. B, 2022, 31(1): 010504.
[2] Stabilization strategy of a car-following model with multiple time delays of the drivers
Weilin Ren(任卫林), Rongjun Cheng(程荣军), and Hongxia Ge(葛红霞). Chin. Phys. B, 2021, 30(12): 120506.
[3] Heterogeneous traffic flow modeling with drivers' timid and aggressive characteristics
Cong Zhai(翟聪), Weitiao Wu(巫威眺), and Songwen Luo(罗淞文). Chin. Phys. B, 2021, 30(10): 100507.
[4] Collective motion of polar active particles on a sphere
Yi Chen(陈奕), Jun Huang(黄竣), Fan-Hua Meng(孟繁华), Teng-Chao Li(李腾超), and Bao-Quan Ai(艾保全). Chin. Phys. B, 2021, 30(10): 100510.
[5] Lagrangian analysis of the formation and mass transport of compressible vortex rings generated by a shock tube
Haiyan Lin(林海燕), Yang Xiang(向阳, Hong Liu(刘洪), and Bin Zhang(张斌). Chin. Phys. B, 2021, 30(3): 030501.
[6] Symmetry properties of fluctuations in an actively driven rotor
He Li(李赫), Xiang Yang(杨翔), Hepeng Zhang(张何朋). Chin. Phys. B, 2020, 29(6): 060502.
[7] Solid angle car following model
Dongfang Ma(马东方), Yueyi Han(韩月一), Sheng Jin(金盛). Chin. Phys. B, 2020, 29(6): 060504.
[8] Reversed rotation of limit cycle oscillation and dynamics of low-intermediate-high confinement transition
Dan-Dan Cao(曹丹丹), Feng Wan(弯峰), Ya-Juan Hou(侯雅娟), Hai-Bo Sang(桑海波), Bai-Song Xie(谢柏松). Chin. Phys. B, 2018, 27(6): 065201.
[9] Hydrophobic nanochannel self-assembled by amphipathic Janus particles confined in aqueous nano-space
Gang Fang(方钢), Nan Sheng(盛楠), Tan Jin(金坦), Yousheng Xu(许友生), Hai Sun(孙海), Jun Yao(姚军), Wei Zhuang(庄巍), Haiping Fang(方海平). Chin. Phys. B, 2018, 27(3): 030505.
[10] Improvement of the thermoelectric efficiency of pyrene-based molecular junction with doping engineering
Mohammad Farid Jamali, Meysam Bagheri Tagani, Hamid Rahimpour Soleimani. Chin. Phys. B, 2017, 26(12): 123101.
[11] Nonlinear density wave and energy consumption investigation of traffic flow on a curved road
Zhizhan Jin(金智展), Rongjun Cheng(程荣军), Hongxia Ge(葛红霞). Chin. Phys. B, 2017, 26(11): 110504.
[12] Uphill anomalous transport in a deterministic system with speed-dependent friction coefficient
Wei Guo(郭伟), Lu-Chun Du(杜鲁春), Zhen-Zhen Liu(刘真真), Hai Yang(杨海), Dong-Cheng Mei(梅冬成). Chin. Phys. B, 2017, 26(1): 010502.
[13] Anomalous transport in fluid field with random waiting time depending on the preceding jump length
Hong Zhang(张红), Guo-Hua Li(李国华). Chin. Phys. B, 2016, 25(11): 110504.
[14] An exclusion process with dynamic roadblocks
Ning Guo(郭宁), Jin-Yong Chen(陈金邕), Mao-Bin Hu(胡茂彬), Rui Jiang(姜锐). Chin. Phys. B, 2016, 25(6): 060505.
[15] Modeling the capability of penetrating a jammed crowd to eliminate freezing transition
Mohammed Mahmod Shuaib. Chin. Phys. B, 2016, 25(5): 050501.
No Suggested Reading articles found!