Mining of interactions between travel demand and land use mixture using multi-source data

Urban spatial structure refers to the relative location relationship and spatial feature formed by the interaction of various elements of the city through its internal mechanism, which is the accumulation of human spatial activities and location selection in the long-term process. The traditional traffic flow begins to transform into a new type of intelligent network mixed traffic flow. In order to explore the coupling coordination relationship between travel demand and land use mixture, multi-source data are used for modeling for travel demand and land use respectively in this paper. A land use mixture identification methodology is proposed based on Shannon’s information entropy weight method, and realizes the intrinsic association of the previous two variables. Taking a case study of Qingdao, China, the results indicate that the dynamic spatio-temporal distribution of taxi travel demand is imbalanced, and hotspots are mostly distributed in transportation hubs and business circles. It also found that the coupling coordination of the land use mixture and travel demand inside the city are spatial heterogeneity and imbalanced. The higher land use mixture attracts more trips, and it is more significant at weekends. The results can provide policy makers with a quantitative method for evaluating the relative spatial gaps for land use intensity to attract more trips.