Affiliation:
1. School of Civil Engineering, Southeast University, Nanjing 211189, China
2. Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
3. Changzhou Transportation Comprehensive Administrative Law Enforcement Detachment, Changzhou 213001, China
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.
Funder
National Natural Science Foundation of China
Subject
Computer Networks and Communications,Computer Science Applications
Cited by
1 articles.
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