Modeling Driver Speed Variability and Profiles at Raised Pedestrian Crossings to Support 30 km/h Traffic Calming Zones: A Methodological Case Study

The implementation of Raised Pedestrian Crossings (RPCs) is one of the most common strategies used to reduce vehicle speeds in urban areas, thereby enhancing road safety levels. The understanding of driver speed profile variability is critical for designing effective traffic-calming policies (such as Zone 30) that account for the full range of driver responses. This study analyzed 19,840 discrete speed measurements collected from 2480 unique drivers at eight longitudinal points approaching and departing an RPC, located in the city of Cassino, Italy. The model takes into account two distinct directional approaches due to the different slopes that characterize the case study road segments that converge at the RPC. To overcome the statistical bias of simple OLS models, a Linear Mixed-Effects Model (LMEM) for each direction has been implemented. This hierarchical approach correctly models the nested data structure (measurements within drivers) and quantifies the variance related to individual driver behavior. Within this LMEM approach, the aim is to evaluate how drivers decelerate and accelerate before and after the RPC. This step is crucial because understanding vehicle and driver behavior on the RPC provides the basis for implementing effective speed reduction strategies, such as the 30 km/h Zone.