Exploring the impact of social economic variables on traffic safety performance in Hong Kong: A time series analysis

Using time series data over the period of 1984-2015, this paper applies the combined autoregressive distributed lag (ARDL) and vector error correction model (VECM) approach to identify short- and long-run causal relationships between the number of road accidents and social economic development, including increase of GDP, growth of population, expansion of road infrastructure, and growth of private car ownership. The results confirm a long run relationship between those four social economic variables and road accidents frequency in which growth of population and private car ownership lead to long-run increases in the number of road accidents while road network expansion leads to a long-run decrease in road accidents number in Hong Kong. Additionally, the correlation inside of social economic variables are examined and reported. Insights that obtained from this study are expected to help with evaluating new policies and programs currently being implemented for verifying if they could contribute to a major improvement of road safety in Hong Kong.