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Hourly traffic flow forecasting using a new hybrid modelling method

一种基于新型混合模型的小时交通流预测方法

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Abstract

Short-term traffic flow forecasting is a significant part of intelligent transportation system. In some traffic control scenarios, obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures. In hence, it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning. This paper proposed a new hybrid model for traffic flow forecasting, which is composed of the variational mode decomposition (VMD) method, the group method of data handling (GMDH) neural network, bi-directional long and short term memory (BILSTM) network and ELMAN network, and is optimized by the imperialist competitive algorithm (ICA) method. To illustrate the performance of the proposed model, there are several comparative experiments between the proposed model and other models. The experiment results show that 1) BILSTM network, GMDH network and ELMAN network have better predictive performance than other single models; 2) VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model. The effect of VMD method is better than that of EEMD method and FEEMD method. To conclude, the proposed model which is made up of the VMD method, the ICA method, the BILSTM network, the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.

摘要

短期交通流量预测是智能交通系统的重要组成部分. 在某些交通控制场景中, 提前获取未来的 交通流量, 有利于公路管理部门有足够的时间制定相应的交通流量控制措施. 因此, 建立一种准确的 短期交通流量预测方法具有重要的意义, 能够为高峰交通流量警告提供依据. 本文提出了一种新的交 通流量混合预测模型VMD-ICA-GMDH-BILSTM-ELMAN, 该模型由VMD算法对历史交通流序列进行 分解, 并利用ICA算法对GMDH-BILSTM-ELMAN集成的网络模型参数进行优化. 为了验证此模型的 预测性能, 在本文所提出的模型和其他模型之间进行了多次比较实验. 实验结果表明; 1) BILSTM网 络, GMDH网络和ELMAN网络具有比其他单个模型更好的预测性能. 2) VMD分解方法的加入可以 显著改善ICA-GMDH-BILSTM-ELMAN模型的预测性能, 且VMD方法的效果优于EEMD和FEEMD. 综上所述, 由VMD分解, ICA优化, BILSTM网络, GMDH网络和ELMAN网络组成的预测模型, 对 交通流序列具有精确的短期预测性能.

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Abbreviations

VMD:

Variational mode decomposition

GMDH:

The group method of data handling

BILSTM:

Bi-directional long and short term memory

ICA:

Imperialist competitive algorithm

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

RMSE:

Root mean square error

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Authors and Affiliations

  1. Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha, 410075, China

    Hui Liu  (刘辉), Xin-yu Zhang  (张馨雨), Yu-xiang Yang  (杨宇翔), Yan-fei Li  (李燕飞) & Cheng-qing Yu  (余澄庆)

  2. School of Mechatronic Engineering, Hunan Agricultural University, Changsha, 410128, China

    Yan-fei Li  (李燕飞)

Authors
  1. Hui Liu  (刘辉)
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  2. Xin-yu Zhang  (张馨雨)
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  3. Yu-xiang Yang  (杨宇翔)
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  4. Yan-fei Li  (李燕飞)
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  5. Cheng-qing Yu  (余澄庆)
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Corresponding author

Correspondence to Yan-fei Li  (李燕飞).

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Foundation item

Project(61873283) supported by the National Natural Science Foundation of China; Project(KQ1707017) supported by the Changsha Science & Technology Project, China; Project(2019CX005) supported by the Innovation Driven Project of the Central South University, China

Contributors

LIU Hui performed proposing forecasting ideas, writing original draft and review. ZHANG Xin-yu performed writing original draft, review, editing and experimental validation. YANG Yu-xiang and YU Cheng-qing replied to reviewers’ comments and revised the final version. LI Yan-fei provided the concept and completed all the submission.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Liu, H., Zhang, Xy., Yang, Yx. et al. Hourly traffic flow forecasting using a new hybrid modelling method. J. Cent. South Univ. 29, 1389–1402 (2022). https://doi.org/10.1007/s11771-022-5000-2

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