Abstract
Along with the prosperity and rapid development of the national economy, the transportation industry has rapidly developed in China. However, overloaded vehicles have been causing frequent traffic accidents. Thus, to alleviate or resolve the corresponding problems associated with highway engineering safety and the market economy, an improved technique for overload management is urgently required. In this study, to analyze the overload data on expressways and highways in China, we developed a machine learning model by comparing the performances of cluster analysis, backpropagation neural network (BPNN), generalized regression neural network (GRNN), and wavelet neural network (WNN) in analyzing global and local time series overload data. In a case study, our results revealed the trends of overloading on highways in Jiangsu Province. Given sufficient data, BPNN performed better than GRNN and WNN. As the amount of training data increased, GRNN performed better, but the runtime increased. WNN had the shortest runtime among the three methods and could reflect the future trends of the overload rate in the monthly data prediction of overload. Our model provides information with potential value for expressway network management departments through data mining. This information could help management departments allocate resources reasonably and optimize the information utilization rate.
Highlights
Along with the prosperity and rapid development of the national economy, the transportation industry has rapidly developed in China
According to the clustering results of the expressways in Jiangsu Province, 6-axle vehicles weighing above 49 t and entering the expressways between 23:00–4:00 and 12:00–14: 00 are more likely to exhibit overloaded behavior, and the overload rate of these vehicles is higher than that of others
We used machine learning to establish a model for highway overrun and overload, considering Jiangsu Province as an example. e characteristics of overloading were summarized by clustering the historical data of overloading, and a forecasting model of overloading with a high fitting degree based on backpropagation neural network (BPNN) was obtained
Summary
Along with the prosperity and rapid development of the national economy, the transportation industry has rapidly developed in China. Overloading continuously causes road damage [1] and traffic accidents [2], disrupts the normal economic order of the logistics market, and affects the healthy development of the highway transportation economy [3]. We explored the overload characteristics through cluster analysis and compared the performances of backpropagation neural network (BPNN), a commonly employed method for training neural network, generalized regression neural network (GRNN), a radial basis function neural network, and wavelet neural network (WNN), a novel neural network that combines classical sigmoid neural networks with wavelet analysis (WA), to determine the best method to mine, analyze, and predict the overload data. Conventional research methods for analyzing the characteristics of overloading in China mainly include literature review, statistical prediction model, and game analysis model. Some scholars study the related characteristics by building data models. It is seen that most of the current research is based on data modeling to conduct data mining; we explore which model should be used in the research as follows
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