Impact of urban road characteristics on vehicle speed: Insights from Brescia, Italy.

Heliyon

Department of Civil, Environmental, Architectural Engineering and Mathematics (DICATAM), University of Brescia, Brescia, Italy.

Published: October 2024

In managing road infrastructures, a key benchmark is the 85th percentile of vehicle speeds (V). While V can be derived from spot speed samples, these are often lacking on each urban road. Thus, prediction models become valuable tools for examining the relationship between V and road characteristics. Although various models exist for rural roads, the impact of roadside characteristics and markings on V in urban road networks has been partially investigated, and the effect of traffic calming measures remains fragmented. This study aims to address these gaps by applying a methodology that sheds light on the effects of some variables that influence V on urban roads. Specifically, the methodology selects and segments roads along the urban road network of the municipality of Brescia (Italy) and collects data on both road characteristics and 48,000+ spot speed information. Following data cleansing, it processes these data according to three different multiple regression models to analyse the influence of various predictors on V Once the best model is estimated, its performance is evaluated, and the final list of significant predictors is obtained. The results revealed that V increases with longer homogeneous segments, greater distance to successive intersections, bituminous conglomerate roads with more lanes, and the presence of trees, visible road markings, and posted speed limits. Conversely, V decreases in the presence of on-street parking and other obstacles (e.g., walls and road posts), when the density of road intersections and pedestrian crossings increases, when the left crossbar width increases and when the land use crossed is commercial or office, residential or industrial. Nevertheless, no significant effect was found for several traffic calming measures included in the model. These findings can assist road authorities in verifying road operating conditions and planning infrastructure interventions to reduce speeds, thereby creating a safer urban environment for all users.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11538745PMC
http://dx.doi.org/10.1016/j.heliyon.2024.e39459DOI Listing

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