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Research on safe driving behavior of transportation vehicles based on vehicle network data mining
Author(s) -
Sun Yuefang,
Bi Yu,
Han Yang,
Xie Dongxue,
Li Ruishan
Publication year - 2020
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3772
Subject(s) - transport engineering , analytic hierarchy process , consistency (knowledge bases) , acceleration , automotive engineering , computer science , engineering , operations research , physics , classical mechanics , artificial intelligence
In recent years, with the rapid development of China's economy and the growth of people's travel demand, road transport industry has developed rapidly. However, at the same time, the traffic safety problems caused by the accelerated growth of transport vehicles are becoming more and more serious. Driver's driving behavior is closely related to traffic safety. With the help of data mining technology, we can make full use of the system data collected by road transportation industry to identify the bad driving behavior of transport vehicles and get seven secondary indicators of traffic safety evaluation indicators: overspeed, accelerated, decelerated, fatigue driving, idle preheating, overspeed idling, and flameout taxiing. According to the indicators, the occurrence times and duration are further subdivided into 14 three‐level indicators. Then, by using the analytic hierarchy process, through the construction of comparison judgment matrix and consistency checking, the corresponding weight of each evaluation index is determined with the help of the entropy method, and the formation safety of transport vehicles is evaluated. The results show that drivers with more fatigue driving, acceleration, and deceleration have higher driving risk. Transportation enterprises and relevant departments need to focus on their monitoring. The combination of data mining, data analysis algorithm, and traffic safety field in machine learning provides a demonstration for the study of safe driving behavior of transport vehicles in China and has a guiding significance for the supervision and training of drivers' driving behavior and reducing road safety risks.