K. Li, K.X. Li, A. Donofrio

Pages: 125-134

Abstract
This paper evaluates and analyzes the correlation between traffic congestion and transportation policies in different cities based on big data analysis method. On the basis of quantitative analysis of urban spatial structure and traffic congestion, an evaluation model for the correlation between traffic congestion and transportation policies is proposed. Based on the indicators of congestion intensity and congestion balance categories, an analysis model for urban spatial structure elements was constructed, and the constraint parameters of traffic congestion related features were calculated using a logistic regression model. According to the regression model, the policy factors affecting traffic congestion were analyzed. Through the big data analysis method of real-time road data, road data, building data and heat map data, the correlation evaluation and quantitative analysis of transportation policy and traffic congestion in different urban scales are realized. The simulation results indicate that when evaluating the correlation between traffic policies and traffic congestion, this method can more accurately quantitatively identify spatial structure and traffic congestion characteristics, reduce travel time, and improve traffic flow levels.
Keywords: city scale; traffic policy; traffic congestion; relevance; heat map data; logistics model