The Use of Macro-Level Safety Performance Functions for Province-Wide Road Safety Management

• Published in: Sustainability 2022-08, Vol.14 (15), p.9245
• Main Authors: Intini, Paolo, Berloco, Nicola, Coropulis, Stefano, Gentile, Roberta, Ranieri, Vittorio
• Format: Article
• Language: English
• Subjects: Bicycles; Cities; Clustering; Datasets; Dependent variables; Elevation; Highways; Indicators; Population; Population density; Provinces; Regional planning; Roads; Rural environments; Safety; Safety management; Towns; Traffic accidents & safety; Traffic congestion; Transportation networks; Transportation planning; Urban areas; Urban environments; Variables
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su14159245

Safety Performance Functions (SPFs) play a key role in identifying hotspots. Most SPFs were built at the micro-level, such as for road intersections or segments. On the other hand, in case of regional transportation planning, it may be useful to estimate SPFs at the macro-level (e.g., counties, cities, or towns) to determine ad hoc intervention prioritizations. Hence, the final aim of this study is to develop a predictive framework, supported by macro-level SPFs, to estimate crash frequencies, and consequently possible priority areas for interventions. At a province-wide level. The applicability of macro-level SPFs is investigated and tested thanks to the database retrieved in the context of a province-wide Sustainable Urban Mobility Plan (Bari, Italy). Starting from this database, the macro-areas of analysis were carved out by clustering cities and towns into census macro-zones, highlighting the potential need for safety interventions, according to different safety performance indicators (fatal + injury, fatal, pedestrian and bicycle crashes) and using basic predictors divided into geographic variables and road network-related factors. Safety performance indicators were differentiated into rural and urban, thus obtaining a set of 4 × 2 dependent variables. Then they were linked to the dependent variables by means of Negative Binomial (NB) count data models. The results show different trends for the urban and rural contexts. In the urban environment, where crashes are more frequent but less severe according to the available dataset, the increase in both population and area width leads to increasing crashes, while the increase in both road length and mean elevation are generally related to a decrease in crash occurrence. In the rural environment, the increase in population density, which was not considered in the urban context, strongly influences crash occurrence, especially leading to an increase in pedestrian and bicyclist fatal + injury crashes. The increase in the rural network length (excluding freeways) is generally related to a greater number of crashes as well. The application of this framework aims to reveal useful implications for planners and administrators who must select areas of intervention for safety purposes. Two examples of practical applications of this framework, related to safety-based infrastructural planning, are provided in this study.