基于多源数据融合的高速公路路网短时交通流参数实时预测

doi:10.13229/j.cnki.jdxbgxb.20221272

摘要/Abstract

摘要：

针对高速公路路网交通数据中存在大量噪声数据与缺失数据，数据完整度较低，导致预测精度下降的问题，提出一种基于多源数据融合的高速公路路网短时交通流参数实时预测方法。采用小波分析阈值法对高速公路路网交通数据进行去噪处理，在最小二乘支持向量机基础上采用组合阈值填补方法填补交通数据序列中存在的缺失数据，提高交通数据的完整度。结合小波神经网络和遗传算法建立短时交通流参数预测模型，采用遗传-小波神经网络处理多源检测器采集到的交通流参数，通过最小二乘动态加权融合算法融合多个检测器的交通流参数，将交通流参数输入预测模型中，得到高速公路路网短时交通流参数实时预测结果。实验结果表明，采用本文方法处理后的交通数据序列中不存在缺失数据，数据完整度较高，且所得预测结果与实际车流量变化曲线较为贴近，预测精度高，可以广泛应用在交通流预测领域。

关键词: 多源数据融合, 高速公路路网, 短时交通流, 参数预测, 小波分析阈值法, 最小二乘支持向量机, 遗传?小波神经网络

Abstract:

There are a lot of noise data and missing data in the traffic data of expressway network， and the data integrity is not high， leading to the decline of prediction accuracy. A real-time prediction method for short-term traffic flow parameters of expressway network based on multi-source data fusion is proposed. The wavelet analysis threshold method is used to denoise the traffic data of the expressway network. Based on the least squares support vector machine， the combined threshold filling method is used to fill in the missing data in the traffic data sequence to improve the integrity of the traffic data. The short-term traffic flow parameter prediction model is established by combining wavelet neural network and genetic algorithm. The traffic flow parameters collected by multi-source detectors are processed by genetic wavelet neural network. The traffic flow parameters of multiple detectors are fused by the least squares dynamic weighted fusion algorithm. The traffic flow parameters are input into the prediction model to obtain the real-time prediction results of short-term traffic flow parameters of expressway network. The experimental results show that there is no missing data in the traffic data series processed by the proposed method， the data integrity is high， and the predicted results are close to the actual vehicle flow change curve， with high prediction accuracy， which can be widely used in the field of traffic flow prediction.

Key words: multi-source data fusion, expressway network, short term traffic flow, parameter prediction, wavelet analysis threshold method, least squares support vector machine, genetic wavelet neural network

中图分类号:

U491.1

高海龙,徐一博,刘坤,李春阳,卢晓煜. 基于多源数据融合的高速公路路网短时交通流参数实时预测[J]. 吉林大学学报(工学版), 2024, 54(1): 155-161.

Hai-long GAO,Yi-bo XU,Kun LIU,Chun-yang LI,Xiao-yu LU. High-speed highway road network short-term traffic flow parameters based on multi-source data fusion prediction[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(1): 155-161.

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参考文献 15

1	薛晗, 邵哲平, 潘家财, 等. 基于文化萤火虫算法-广义回归神经网络的船舶交通流量预测[J]. 上海交通大学学报, 2020, 54(4): 421-429.
	Xue Yi, Shao Zhe-ping, Pan Jia-cai, et al. Vessel traffic flow prediction based on CFA-GRNN algorithm[J]. Journal of Shanghai Jiaotong University, 2020, 54 (4): 421-429.
2	林艳, 闫帅, 张一晋, 等. 基于交通流量预测的车联网双边拍卖边缘计算迁移方案[J]. 通信学报, 2020,41(12): 205-214.
	Lin Yan, Yan Shuai, Zhang Yi-jin, et al. Flow-of-traffic prediction program based mobile edge computing for Internet of vehicles using double auction[J]. Journal on Communications, 2020, 41(12): 205-214.
3	刘小明, 田玉林, 唐少虎, 等. 基于时延特性建模的多断面短时交通流预测[J]. 交通运输系统工程与信息, 2020, 20(3): 54-60.
	Liu Xiao-ming, Tian Yu-lin, Tang Shao-hu, et al. Short-term traffic flow prediction of multi-sections based on time-delay modeling[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(3): 54-60.
4	倪庆剑, 彭文强, 张志政, 等. 基于信息增强传输的时空图神经网络交通流预测[J]. 计算机研究与发展,2022, 59(2): 282-293.
	Ni Qing-jian, Peng Wen-qiang, Zhang Zhi-zheng, et al. Spatial-temporal graph neural network for traffic flow prediction based on information enhanced transmission[J]. Journal of Computer Research and Development, 2022, 59(2): 282-293.
5	陈克, 张晓冬, 李宁. 基于CEEMD与自适小波阈值组合降噪在OPAX方法的应用[J]. 振动与冲击,2021, 40(16): 192-198.
	Chen Ke, Zhang Xiao-dong, Li Ning. Application of CEEMD and adaptive wavelet threshold combined noise reduction in the OPAX method[J]. Journal of Vibration and Shock, 2021, 40 (16): 192-198.
6	田劼, 宋姗. 改进粒子群优化小波阈值的矿用钢丝绳损伤信号处理方法研究[J]. 煤炭工程, 2020, 52(4):103-107.
	Tian Yan, Song Shan. Processing method for mine wire rope damage signal based on improved particle swarm optimization wavelet threshold[J]. Coal Engineering, 2020, 52(4): 103-107.
7	王萍, 张吉昂, 程泽. 基于最小二乘支持向量机误差补偿模型的锂离子电池健康状态估计方法[J]. 电网技术, 2022, 46(2): 613-623.
	Wang Ping, Zhang Jiang, Cheng Ze. State of health estimation of li-ion battery based on least squares support vector machine error compensation model[J]. Power System Technology, 2022, 46(2): 613-623.
8	舒星, 刘永刚, 申江卫, 等. 基于改进最小二乘支持向量机与Box-Cox变换的锂离子电池容量预测[J].机械工程学报, 2021, 57(14): 118-128.
	Shu Xing, Liu Yong-gang, Shen Jiang-wei, et al. Capacity prediction for lithium-ion batteries based on improved least squares support vector machine and Box-Cox transformation[J]. Journal of Mechanical Engineering, 2021, 57 (14): 118-128.
9	李兵, 崔介兵, 何怡刚, 等. 基于能量谱熵及小波神经网络的有源中性点钳位三电平逆变器故障诊断[J].电工技术学报, 2020, 35(10): 2216-2225.
	Li Bing, Cui Jie-bing, He Yi-gang, et al.. Fault diagnosis of active neutral point clamped three-level inverter based on energy spectrum entropy and wavelet neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(10): 2216-2225.
10	韩锟,李斯宇.基于遗传小波神经网络的停车泊位预测方法[J].铁道科学与工程学报,2020,17(9): 2216-2224.
	Han Yan, Li Si-yu. Parking space prediction method based on genetic wavelet neural network[J]. Journal of Railway Science and Engineering, 2020,17(9): 2216-2224.
11	梁仲明, 滕宪斌, 杨期江, 等. 基于CEEMD-参数优化Morlet小波变换的柴油机振动信号冲击特征提取方法[J]. 船舶工程, 2020, 42(5): 90-96.
	Liang Zhong-ming, Teng Xian-bin, Yang Qi-jiang, et al. Diesel engine vibration signal impact feature extraction method based on CEEMD-parameter optimized morlet wavelet transform[J]. Ship Engineering, 2020, 42(5): 90-96.
12	郭昌放, 杨真, 武祥, 等. 基于Morlet小波的时频分析方法在地震槽波精准反演煤厚中的应用[J]. 中国煤炭, 2021, 47(5): 46-52.
	Guo Chang-fang, Yang Zhen, Wu Xiang, et al. Application of time-frequency analysis method based on Morlet wavelet in accurate inversion of coal seam thickness by seismic channel wave[J]. China Coal, 2021, 47(5): 46-52.
13	李博文,谢在鹏,毛莺池,等. 一种基于分布式编码的同步梯度下降算法[J]. 计算机工程, 2021, 47(4):68-76, 83.
	Li Bo-wen, Xie Zai-peng, Mao Ying-chi, et al. A synchronized gradient descent algorithm based on distributed coding[J]. Computer Engineering, 2021,47(4): 68-76, 83.
14	宋杰, 朱勇, 许冰. 批量减数更新方差缩减梯度下降算法BSUG[J].计算机工程与应用,2020,56(22):117-123.
	Song Jie, Zhu Yong, Xu Bing. Batch subtraction update variance reduction gradient descent algorithm BSUG[J]. Computer Engineering and Applications, 2020, 56 (22): 117-123.
15	潘龙帅, 高建平, 宋哲, 等. 多源信息融合的车速预测方法及整车能量管理[J]. 河南科技大学学报:自然科学版, 2020, 41(6): 23-31, 4,5.
	Pan Long-shuai, Gao Jian-ping, Song Zhe, et al. Vehicle speed prediction method based on multi-source information fusion and vehicle energy management[J]. Journal of Henan University of Science & Technology (Natural Science), 2020, 41(6): 23-31, 4,5.

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实验环境	配置	参数
硬件环境	CPU	Intel（R） Core（TM） i5-9400
	频率	2.90 GHz
	RAM	16.0 GB
软件环境	操作系统	Windows 10
	版本	18362.1082专业版
	位数	64 bit
	模拟软件语言	APDL
	仿真软件	Matlab 7.0

数据类型	数据量
基础空间数据	7.06
城市及周边基础地理信息	5.69
道路交通网络基础信息	7.86
道路交通客运信息	10.50
停车场信息	11.20
交通管理信息	5.96
遥感信息	6.87
智慧交通系统信息	8.78
车联网信息	5.66

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