Development of Macro-Level Safety Performance Functions in the City of Naples

Alfonso Montella, Vittorio Marzano, Filomena Mauriello, Roberta Vitillo, Roberto Fasanelli, Mariano Pernetti and Francesco Galante
Additional contact information
Alfonso Montella: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Vittorio Marzano: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Filomena Mauriello: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Roberta Vitillo: Department of Business and Quantitative Studies, University of Naples Parthenope, 80132 Naples, Italy
Roberto Fasanelli: Department of Social Sciences, University of Naples Federico II, 80138 Naples, Italy
Mariano Pernetti: Department of Engineering, University of Campania “Luigi Vanvitelli”, 81031 Aversa (Caserta), Italy
Francesco Galante: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy

Sustainability, 2019, vol. 11, issue 7, 1-21

Abstract: This paper presents macro-level safety performance functions and aims to provide empirical tools for planners and engineers to conduct proactive analyses, promote more sustainable development patterns, and reduce road crashes. In the past decade, several studies have been conducted for crash modeling at a macro-level, yet in Italy, macro-level safety performance functions have neither been calibrated nor used, until now. Therefore, for Italy to be able to fully benefit from applying these models, it is necessary to calibrate the models to local conditions. Generalized linear modelling techniques were used to fit the models, and a negative binomial distribution error structure was assumed. The study used a sample of 15,254 crashes which occurred in the period of 2009–2011 in Naples, Italy. Four traffic analysis zones (TAZ) levels were used, as one of the aims of this paper is to check the extent to which these zoning levels help in addressing the issue. The models were developed by the stepwise forward procedure using explanatory Socio-Demographic (S-D), Transportation Demand Management (TDM), and Exposure variables. The most significant variables were: children and young people placed in re-education projects, population, population aged 65 and above, population aged 25 to 44, male population, total vehicle kilometers traveled, average congestion level, average speed, number of trips originating in the TAZ, number of trips ending in the TAZ, number of total trips and, number of bus stops served per hour. An important result of the study is that children and young people placed in re-education projects negatively affects the frequency of crashes, i.e., it has a positive safety effect. This demonstrates the effectiveness of education projects, especially on children from disadvantaged neighbourhoods.

Keywords: safety prediction models; traffic analysis zone; negative binomial; traffic crashes (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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