Spatiotemporal and Vehicle-Class Modeling of Urban Speed Distributions

Urban speed management requires distributional rather than mean-only assessment because averages can conceal upper-tail speeding, vehicle-class differences, and multiple operating regimes. This study applies an integrated sensor-based analytical framework to characterize spatial, temporal, and vehicle-class heterogeneity in urban operating-speed distributions in Riyadh and to provide reusable indicators for targeted speed management, traffic-flow calibration, and future camera–effect evaluation. More than 2.3 million classified vehicle passages recorded over seven days at five automatic sensor locations were analyzed. The sensors were situated upstream of speed camera positions; this describes spatial placement only, as matched downstream observations were unavailable. Grouped distributional measures, frequency-weighted harmonic fixed-effects regression, and Gaussian mixture models were used to examine compliance, diurnal patterns, and latent regimes. Results show that network-wide averages can suggest general compliance while upper-tail measures reveal localized speeding, with higher speeds for cars than for heavy vehicles and substantial spatial and directional heterogeneity. At one of the analyzed sensor locations (B03), the speed distribution was distinctly multimodal, indicating coexisting low- and high-speed regimes. A parsimonious three-harmonic model reproduced the main daily speed cycle. The resulting indicators and time-dependent speed profile can support targeted monitoring and intervention, serve as inputs for traffic-flow models, and provide a baseline for future camera–effect studies using matched upstream/downstream or temporal before/after observations. With repeated observations and local validation, these results can also provide a common baseline for assessing whether future engineering and enforcement measures reduce high-speed exposure across vehicle classes.