Open AccessEmpirical Analysis on Influencing Factors of Traffic Casualties in China
Author(s) -
Li Sun,
Jiamin Zhang,
Feng Yu,
Mengwei Chen
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/820/1/012030
Subject(s) - transport engineering , china , population , urbanization , business , index (typography) , geography , investment (military) , engineering , computer science , economic growth , environmental health , economics , medicine , political science , archaeology , world wide web , politics , law
In order to further explore the complex correlation between the influencing factors of road traffic accidents in China, this paper takes the road traffic safety-related data of 31 provinces from 2004 to 2018 as the sample data, selects five index data that may affect the occurrence of traffic accidents in various regions of the country, i.e. resident population, urban population, population density, GDP and number of car drivers, and uses Eviews software for stepwise regression analysis to establish a fixed effect model of the influencing factors of traffic accidents and analyze the macro and micro factors that affect the death toll of traffic accidents in China. The model shows that each index has a significant impact on the number of road traffic casualties. Urban population, population density and number of car drivers are positively correlated with the number of road traffic casualties. The resident population and GDP are negatively correlated with the number of road traffic casualties. According to the research conclusions, this paper puts forward some suggestions, such as optimizing the management technology of road traffic construction, strengthening the education system of traffic safety awareness of residents in the new urbanization, increasing the investment in traffic construction, and establishing an institution for differentiated training and management of drivers based on driving experience.