Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (1): 155-161.doi: 10.13229/j.cnki.jdxbgxb.20221272

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High-speed highway road network short-term traffic flow parameters based on multi-source data fusion prediction

Hai-long GAO1(),Yi-bo XU1,Kun LIU2,Chun-yang LI1,Xiao-yu LU1   

  1. 1.Safety Center，Research Institute of Highway Ministry of Transport，Beijing 100088，China
    2.Hong Kong -Zhuhai -Macao Bridge Authority，Zhuhai 519060，China
  • Received:2022-10-12 Online:2024-01-30 Published:2024-03-28

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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

CLC Number: 

  • U491.1

Fig.1

Highway road network short-time traffic flow parameter real-time prediction flowchart"

Table 1

Experimental environment parameters"

实验环境配置参数
硬件环境CPUIntel（R） Core（TM） i5-9400
频率2.90 GHz
RAM16.0 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

Table 2

Experimental data"

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

Fig.2

Highway road network"

Fig.3

Missing traffic data sequence"

Fig.4

Data processing results of different methods"

Fig.5

Prediction results of traffic flow parameters ofdifferent methods"

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