Spatial point pattern analysis of traffic violations in Luzhou City, China

Abstract

ABSTRACT The study presents spatial analysis of traffic violations in Luzhou city, China. Three metrics of spatial point pattern, namely intensity, spatial correlation, and spacing, are evaluated using (i) kernel density and quadrant count; (ii) K and L functions; and (iii) nearest neighbor and empty-space distance methods, respectively. The results show that: (i) three specific places have a high rate of illegal turning movements, disobeying prohibited signs, and illegal parking violations; (ii) all the violation categories have hot spots, among which 53.49% of the total appear at nine different places; (iii) the spatial correlation discloses the existence of spatial dependence and closeness of similar violations; and (iv) the spacing between traffic violations also signifies clustering of violations. Generally, the spatial patterns of traffic violations are nonuniform, have identifiable hot spots, and are clustered. The findings are beneficial for conceiving safety treatment strategies against traffic violations and additional relevant clustering studies.