The variability of urban safety performance functions for different road elements: an Italian case study

• Published in: European transport research review 2021-12, Vol.13 (1), p.1-14, Article 30
• Main Authors: Intini, Paolo, Berloco, Nicola, Cavalluzzi, Gabriele, Lord, Dominique, Ranieri, Vittorio, Colonna, Pasquale
• Format: Article
• Language: English
• Subjects: Automotive Engineering; Civil Engineering; Crashes; Engineering; Injury crash; Modelling; Original Paper; Prediction models; Regional/Spatial Science; Research projects; Road maintenance; Roads; Safety; Safety performance functions; Segments; Traffic control; Traffic intersections; Traffic signs; Transportation; Trends; Urban intersections; Urban segments; Variability
• ISSN: 1867-0717; 1866-8887
• DOI: 10.1186/s12544-021-00490-6

Background Urban safety performance functions are used to predict crash frequencies, mostly based on Negative Binomial (NB) count models. They could be differentiated for considering homogeneous subsets of segments/intersections and different predictors. Materials and methods The main research questions concerned: a) finding the best possible subsets for segments and intersections for safety modelling, by discussing the related problems and inquiring into the variability of predictors within the subsets; b) comparing the modelling results with the existing literature to highlight common trends and/or main differences; c) assessing the importance of additional crash predictors, besides traditional variables. In the context of a National research project, traffic volumes, geometric, control and additional variables were collected for road segments and intersections in the City of Bari, Italy, with 1500 fatal+injury related crashes (2012–2016). Six NB models were developed for: one/two-way homogeneous segments, three/four-legged, signalized/unsignalized intersections. Results Crash predictors greatly vary within the different subsets considered. The effect of vertical signs on minor roads/driveways, critical sight distance, cycle crossings, pavement/markings maintenance was specifically discussed. Some common trends but also differences in both types and effect of crash predictors were found by comparing results with literature. Conclusion The disaggregation of urban crash prediction models by considering different subsets of segments and intersections helps in revealing the specific influence of some predictors. Local characteristics may influence the relationships between well-established crash predictors and crash frequencies. A significant part of the urban crash frequency variability remains unexplained, thus encouraging research on this topic.