Abstract:

Traffic state and road network structure have a complex interaction relationship, which affects people's travelling efficiency and satisfaction. In this paper, the number of GPS points and speed of road sections to the speed of free flow at night is taken as an indicator of traffic state, and the coupling relationship between traffic state and the degree of road sections, betweenness centrality, closeness centrality and eigenvector centrality is explored by using the coupling coordination degree model. With the coupling relationship as the dependent variable, thirteen independent variables were selected to regress the mechanism of the coupling degree using CatBoost combined with SHAP. The results show that: the number of intersections, green area, and the number of bus stops promote the coupling coordination between traffic status and road network structure, and the road separation type has a significant positive effect on the degree of coupling between traffic state and betweenness centrality, closeness centrality, and degree; for the positively uncoordinated roads, the intensity of public management land and residential land have a negative effect on the coupling coordination between traffic state and closeness centrality. For positively uncoordinated roads, the intensity of public management land and residential land has a negative effect on the coupling of traffic state with closeness centrality, and the intensity of residential land has a negative effect on the coupling of traffic state with eigenvector centrality; for negatively uncoordinated roads, the intensity of residential land has a negative effect on the coupling of traffic state with closeness centrality. The research results can provide new ideas for the planning, optimisation and adjustment of road network structure.

Key words: road significance, traffic state, CatBoost model, coupling coordination degree; taxi GPS trajectory data