The built environment elements in urban areas can have a significant impact on the performance of transport systems, including road safety. The primary objective of this research is to investigate the influence of the built environment on speeding behavior, as an indicator of road safety performance, using the city of Curitiba, Brazil, as the study's setting. The built environment comprises physical features within the city, such as development patterns and roadway designs, and can be categorized into six groups: density, diversity, design, destination accessibility, distance to transit, and demographics. The Geographically Weighted Regression (GWR) statistical model was employed to explore the correlation between built environment variables and the occurrence of speeding in a spatially nonstationary scenario. Additionally, Moran's I and Local Moran statistical methods were applied to investigate the spatial autocorrelation of speeding within the city. Data on speeding and location were collected through the application of a Naturalistic Driving Study. The measure of speeding was based on free-flow situations, considering the opportunity in which drivers could speed. In this study, the database included 1002 trips, 381.45 h of driving, and 9,443.83 km of travel within Curitiba and its metropolitan area from 2019 to 2021. The GWR model was applied using Curitiba's traffic analysis zones (TAZs) as the zonal level. GWR reduced residual spatial autocorrelation relative to the global linear model; however, the global model achieved a lower AICc. Only the variable “proportion of arterial roads” showed a statistically significant correlation with speeding at a 95 % confidence level, with an inverse correlation observed across 100 % of the TAZs. Furthermore, it was observed that speeding behavior in Curitiba exhibits spatial autocorrelation, justifying the use of the GWR model. Low-Low and High-High spatial clusters were identified, with statistically significant differences observed between them, including average income, density of commercial and service units, density of intersections, density of speed cameras, and traffic signal density. The characteristics of arterial roads in Curitiba, including infrastructure and traffic control, may be influencing the reduction of speeding behavior.