G Chi1, S Wang, R Liu. 1. Medical College of Jinan University, Guangzhou 510632, China.
Abstract
OBJECTIVE: To explore the influence of traffic environment on road injury so as to provide basis for prevention and control. METHODS: Principal component regression analysis was used to explain the relationship between road injury and the numbers of vehicle, capacity of road transport and mileage of rigid highway. RESULTS: Results showed a positive correlation between level of personal safety (LPS) and the numbers of vehicle, volume of road haulage, volume of passenger transport, mileage of rigid highway (correlation coefficients were 0.8714, 0.9691, 0.9611, 0.9510, P = 0.0005). The numbers of vehicle, volume of road haulage, volume of passenger transport and mileage of rigid highway were increasing when LPS decreased. Principal component regression analysis overcame multi collinearty of independent variables and obtained a regression equation y =-3.7197 + 1.49E-03X(1) + 5.2E-06X(3) + 6.19E-02X(4). CONCLUSION: The primary determinants of LPS were numbers of vehicle, volume of road haulage, volume of passenger and mileage of rigid highway. Road injury thus could be reduced through improving the traffic environment, strengthening the traffic administration as well as promoting traffic safety.
OBJECTIVE: To explore the influence of traffic environment on road injury so as to provide basis for prevention and control. METHODS: Principal component regression analysis was used to explain the relationship between road injury and the numbers of vehicle, capacity of road transport and mileage of rigid highway. RESULTS: Results showed a positive correlation between level of personal safety (LPS) and the numbers of vehicle, volume of road haulage, volume of passenger transport, mileage of rigid highway (correlation coefficients were 0.8714, 0.9691, 0.9611, 0.9510, P = 0.0005). The numbers of vehicle, volume of road haulage, volume of passenger transport and mileage of rigid highway were increasing when LPS decreased. Principal component regression analysis overcame multi collinearty of independent variables and obtained a regression equation y =-3.7197 + 1.49E-03X(1) + 5.2E-06X(3) + 6.19E-02X(4). CONCLUSION: The primary determinants of LPS were numbers of vehicle, volume of road haulage, volume of passenger and mileage of rigid highway. Road injury thus could be reduced through improving the traffic environment, strengthening the traffic administration as well as promoting traffic safety.
Authors: Duan Leilei; Ye Pengpeng; Juanita A Haagsma; Jin Ye; Wang Yuan; Er Yuliang; Deng Xiao; Gao Xin; Ji Cuirong; Wang Linhong; Marlena S Bannick; W Cliff Mountjoy-Venning; Caitlin N Hawley; Zichen Liu; Mari Smith; Spencer L James; Theo Vos; Christopher J L Murray Journal: Lancet Public Health Date: 2019-09