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.

Highlights

  • Road safety has been increasingly regarded as one of the most important transportation concerns in urban areas

  • Analysis of the results shows that the goodness of fit of the models improves with decreasing the number of traffic analysis zones (TAZ), R2α increases and Akaike information criterion (AIC) decreases, except for Cptw, Coff-peak day, and Coff-peak night, where going from 208 to 107 TAZ R2α decreases

  • Macro-level safety performance functions were developed in this study to provide decision support tools for planners to consider safety in the transportation planning process, and to promote more sustainable land use and transport patterns

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Summary

Introduction

Road safety has been increasingly regarded as one of the most important transportation concerns in urban areas. Explanatory variables used in previous studies can be grouped in four classes [15]: (1) traffic characteristics (Exposure), (2) social demographic factors (SD), (3) roadway factors (Network), and (4) land use and travel habits (Transportation Demand Management) It is not the most significant predictor of crashes, exposure is a key determinant of traffic safety. Since macro-level has not been calibrated nor used until now in Italy, this paper’s aim is to fill these research gaps by developing safety performance functions to investigate the relationship between crash frequency and their contributing factors at TAZs level, using data from Naples, Italy In this way, the paper provides Italian local and state transportation agencies with tools to conduct proactive road safety planning. Naples is the third-largest municipality in Italy, with an area equal to 117,27 km, with 960,000 inhabitants, and a very high density—equal to 8157.79 inhabitants per km

Traffic Analysis Zones
Crash Data
The Explanatory Variables
Model Description
Measuring Goodness of Fit
Results and Discussion
Conclusions

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