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This study proposes a systematic analysis of traffic violations and crashes at urban intersections. The first step is the spatial analysis of traffic violations and crashes using crash data and traffic violation data. The spatial characteristics of traffic violations and crashes are investigated by integrating the time–space cube model, spatial autocorrelation analysis, and the emerging hotspot analysis method. The spatial methods identify the spatiotemporal hotspots of traffic violations and crashes, and the result illustrates the traffic violations are spatially associated with crashes. The second step aims to further identify the factors influencing the frequency of traffic violations. The traffic violation data and other traffic data are analyzed using an improved negative binomial regression model. The results illustrate AADT, average speed, signal cycle, and signal phase are significantly associated with the frequency of traffic violations. The results are beneficial for alleviating traffic violations and reducing crashes of intersections.

A systematic approach for evaluating spatiotemporal characteristics of traffic violations and crashes at road intersections: an empirical study