Research On Traffic Vibration Signal Detection Based On Seismic Wave Correlation Theory

In 2020,transportation as a fundamental sector of the national economy,was also affected by the COVID-19 epidemic.In this paper,we study the seismic signals and obtain accurate "traffic vibration signals" by means of filtering and noise reduction.The characteristics of the "traffic vibration signal" were also analysed in relation to the traffic control measures taken by Beijing during each phases of the epidemic.It is demonstrated that the "traffic vibration signal" can be used to reflect traffic travel patterns and trends.In this paper,I filter the seismic signals in Beijing from 2018 to 2020 with a bandpass filter in order to filter out the "traffic vibration signals" in the frequency range of 1-10 Hz.Due to the low accuracy of this signal,it needs to be noise reduced again,and the wavelet threshold noise suppression method is used to complete the noise suppression.And the choice of the correct wavelet basis function,the decomposition layers and the threshold function is the crucial point and the difficulty that is decisive for the degree of noise suppression.In most previous wavelet studies,a lot of time was spent in selecting these three parameters.In order to improve the screening efficiency of these three parameters and to obtain the optimal choice of parameters in the wavelet threshold noise reduction process to improve the accuracy of wavelet threshold noise reduction,this paper proposes a multi-indicator fusion of wavelet threshold denoising optimal parameter selection method.Since signal-to-noise ratio,smoothing,and mean square error are the benchmarks used to judge the noise reduction performance of the wavelet threshold noise reduction performance,the algorithm uses the CRITIC weight model to calculate the weights of these three indexes separately,and combines the respective weights of the three indexes with each index linearly,finally obtains the score of each parameter combination scheme,and the parameter combination with the highest score is the optimal parameter selection.Finally,the noise-reduced traffic vibration signals were plotted as power spectral density and compared with the representation of the power spectral density before the epidemic outbreak.At the same time,a comprehensive analysis of the traffic control policies introduced during the epidemic period and the passenger volume of various modes of transport in Beijing from 2019 to 2020 was carried out to plot the time series of traffic flow of various modes of transport during the two years,which further demonstrates that the "traffic vibration signal" in the seismic signal is generated by urban transport,and The study of the "traffic vibration signal" can be used to analyse the changes in traffic flow.Through the method proposed in this paper,the data of "traffic vibration signal" can be used to get a quick overview of the general pattern of traffic changes in a region,which not only solves the difficult problem of collecting traffic flow data of various means of transportation,but also reduces the installation of collecting equipment,and can systematically assist scholars to study the traffic change pattern of the whole region,which provides a new method for studying the change pattern of regional traffic.