TY - JOUR
T1 - Analysis and Prediction of Overloaded Extra-Heavy Vehicles for Highway Safety Using Machine Learning
AU - Lin, Yi Hsin
AU - Gu, Suyu
AU - Wu, Wei Sheng
AU - Wang, Rujun
AU - Wu, Fan
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant Number: 71573037) and the Priority Academic Program Development of Jiangsu Higher Education Institutions in China (Grant Number: 1105007002).
Publisher Copyright:
© 2020 Georgii S. Vasyliev et al.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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U2 - 10.1155/2020/6667897
DO - 10.1155/2020/6667897
M3 - Article
AN - SCOPUS:85099308730
SN - 1574-017X
VL - 2020
JO - Mobile Information Systems
JF - Mobile Information Systems
M1 - 6667897
ER -