关键词: 交通工程, 空间异质性, 半变异函数, 共享出行, 驱动因素, 热点探测

Abstract: This paper examines the spatial heterogeneity characteristics of bike-sharing trips and the driven factor assessment based on trajectory data mining. The kernel density analysis and hotspot detection are performed to obtain the sampled analysis area and characterize the generation of bike-sharing travel with hot values to reduce the interference of scale effects. The semi-variance function of spatial statistics is introduced to simulate the structural and stochastic variation patterns of bike-sharing travel generation, to explore the spatial heterogeneity characteristics and to determine the scale range of neighborhood effects. The slope of the spatial series is used to characterize the change trend. Meanwhile, the respective driving forces of land use, neighborhood effects and other built environment on spatial heterogeneity are distinguished through the improved spatial lag and residual models. The bike- sharing in Beijing is analyzed as a case study. The results indicate that the spatial autocorrelation of bike-sharing trips in Beijing is moderate and positive, and the spatial heterogeneity is consistent with the exponential model. The decay radius of spatial autocorrelation is 1860 meters, and the neighborhood effect becomes insignificant when the distance is greater than this threshold. The built environment has the most significant impact on the spatial heterogeneity of bike-sharing trips, the neighborhood effect is at an intermediate level, and the land use has the smallest impact on bike-sharing trips.

Key words: traffic engineering, spatial heterogeneity, semi- variation function, shared travel, driving factors, hot spot detection