Spatial heterogeneity and migration characteristics of traffic congestion—A quantitative identification method based on taxi trajectory data

Abstract It is of great reference significance to exploring spatial dependence of urban traffic activities and researching internal causes of regional traffic state changes for road network optimization and residents’ travel behavior analysis. Based on trajectory data of taxis in Ningbo city of China, this study calculates average driving speed of taxis in different blocks during characteristic period and generates the global Moran’s I and the LISA clustering diagram. On this basis, the spatial clustering characteristics of congestion on working days and non-working days are analyzed. Furthermore, in order to further characterize the changes of congestion from the perspective of spatial migration, a method of measuring geometric displacement is adopted to describe spatio-temporal migration trend of traffic states, four indicators designed to identify urban frequently congested areas, including migration direction, angle, distance, and low-value area. The results show that the high-clustering area are located urban fringe and the low-clustering area are located at geometric center of major urban areas. Spatial–temporal migration law of low-value areas in city-center is obvious. Difference between trend is compared with non-working days, the offset and azimuth of low-value area in downtown on working days are even bigger. The accurate capture of the characteristics of congestion space migration at the urban scale will help to formulate more targeted congestion management strategies.