Identification of Hazardous Driving Hotspots of Conventional Urban Bus Based on Spatial Autocorrelation

In order to obtain the spatial distributing characteristics of hazardous bus driving status, this paper identified the spatial clustering through spatial autocorrelation analysis, determined the hot spots, and analyzed the significant influencing factors. Firstly, the study collected position system data samples of the urban buses for one week in each of the four quarters and modified the duplicate, abnormal and missing data. Bus stops were used as nodes to divide spatial spots, and every spot was numbered. Over speed, urgent acceleration, urgent deceleration and sharp turn were identified as hazardous driving status. The four conditions thresholds were obtained according to the kinematic characteristics of vehicles. The study calculated statistical indicators and global Moran’s I_g of four conditions. The results show that hazardous driving status are spatially clustered (probability of a spatial random distribution p ＜ 0.01, standard deviation score Z ＞ 2.58). Over speed has most significant characteristic of spatial clustering (I_g = 0.731). The study performed local spatial autocorrelation analysis for the four conditions. According to the analysis, local Moran’s I scatter plots and LISA clustering plots are plotted at 90%, 95% and 99% confidence levels. The hazardous hot spots of urban buses were obtained combining with city maps. Finally, the study selected 9 factors such as road length, number of lanes and straightness to formulate models. The compare and analysis were performed to get the fitting goodness of OLS, SLE, SEM and SDM model. The SDM model was used to obtain the significant influencing factors for 4 dangerous driving states. The results can provide a theoretical basis for supervising the safety operation and identifying the hazardous driving status of urban buses in spatial perspective.