  The macro influencing factors analysis of urban traffic safety is important
to guide the direction of urban development to reduce the frequency of traffic
accidents. In this study, a Bayesian vector autoregressive(BVAR) model was
developed to exploring the impact of six macro-level economic and transport
factors, including population, GDP, private vehicle ownership, bus ownership,
subway rail mileage and road average speed on traffic accidents with the small
sample size transport annual report data in Beijing. The results show that the
BVAR model was suitable for time series analysis of traffic accidents in small
sample situations. In macroeconomic factors, GDP growth was considered to
reduce the number of traffic accidents in the long term, while population
growth had a positive effect on traffic accidents in the short term. With the
respect to macro-transport factors, road average speed and private vehicle
ownership was perceived to increase traffic accidents in long duration, whereas
bus ownership and subway rail mileage had long-term negative effects, with the
greatest positive effect for road average speed and the greatest negative
effect for subway rail mileage. This study suggests that government departments
can reduce the number of traffic accidents by increasing investment in public
transportation infrastructures, limiting private vehicles and road speed.
