Road Safety Analysis Using Operating Speeds: Case Studies in Southern Italy (original) (raw)
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European Transport Research Review, 2021
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...
Road Safety Analysis of Urban Roads: Case Study of an Italian Municipality
Safety, 2018
Attention to the most vulnerable road users has grown rapidly over recent decades. The experience gained reveals an important number of fatalities due to accidents in urban branch roads. In this study, an analytical methodology for the calculation of urban branch road safety is proposed. The proposal relies on data collected during road safety inspections; therefore, it can be implemented even when historical data about traffic volume or accidents are not available. It permits us to identify geometric, physical, functional, and transport-related defects, and elements which are causal factors of road accidents, in order to assess the risk of death or serious injuries for users. Traffic volume, average speed, and expected consequences on vulnerable road users in case of an accident allow us to calculate both the level of danger of each homogeneous section which composes the road, and the hazard index of the overall branch. A case study is presented to implement the proposed methodolog...
Sustainability, 2020
There is no definite conclusion about what the main variables that play a fundamental role in road safety are. Therefore, the identification of significant factors in road accidents has been a primary concern of the transportation safety research community for many years. Every accident is influenced by multiple variables that, in a given time interval, concur to cause a crash scenario. Information coming from crash reports is very useful in traffic safety research, and several reported crash variables can be analyzed with modern statistical methods to establish whether a classification or clustering of different crash variables is possible. Hence, this study aims to use stochastic techniques for evaluating the role of some variables in accidents with a clustering analysis. The variables that are considered in this paper are light conditions, weekday, average speed, annual average daily traffic, number of vehicles, and type of accident. For this purpose, a combination of particle swarm optimization (PSO) and the genetic algorithm (GA) with the k-means algorithm was used as the machine-learning technique to cluster and evaluate road safety data. According to a multiscale approach, based on a set of data from two years of crash records collected from rural and urban roads in the province of Cosenza, 154 accident cases were accurately investigated and selected for three categories of accident places, including straight, intersection, and other, in each urban and rural network. PSO had a superior performance, with 0.87% accuracy on urban and rural roads in comparison with GA, although the results of GA had an acceptable degree of accuracy. In addition, the results show that, on urban roads, social cost and type of accident had the most and least influence for all accident places, while, on rural roads, although the social cost was the most notable factor for all accident places, the type of accident had the least effect on the straight sections and curves, and the number of vehicles had the least influence at intersections.
A New Methodology for Accidents Analysis: The Case of the State Road 36 in Italy
International Journal of Transport Development and Integration, 2021
Every year more than 1.35 million people die for road accidents and several million suffer serious injuries, which force them to live with compromised health conditions. Over the last decades, road safety research has focused on improving modelling techniques. However, due to the lack of expertise and statistical skills, such approaches might not be used by local authorities and road managers for road safety evaluation purposes. This paper proposes an operational methodology to analyze road accidents with the aim of increasing road safety. More specifically, the methodology enables to identify the most critical road segments to prioritize economic resources allocation accordingly. by using the data collected by the Road Police Department of Lombardy Region (in Italy) from 2014 to 2018, this methodology has been successfully applied to State Road 36, which is recognized as one of the busiest roads in Italy with a very high number of accidents occurring every year. The proposed methodology may support public administrations and road managers-involved in the definition and implementation of safety measures-to reduce the number of road accidents identifying and implementing prioritized interventions. Moreover, the methodology is general enough to be applied to each segment of a generic road infrastructure.
Speed prediction models for sustainable road safety management
Procedia - Social and Behavioral Sciences, 2011
This paper illustrates an experimental analysis conducted in 2010 on statistically significant number of roadway sections belonging to two-lane rural roads in Northern Italy. The aim of this research is to develop operating speed prediction models on tangents and circular curves to perform roadway alignment consistency analysis for travel safety in context with current operating speeds. Acquired relationships were particularly interesting and different explanatory variables were introduced in the predictive models which are dependent on examined geometric roads features. These relationships constitute a new set of models about the operating speeds to design and verify roads geometric alignments adding to those already available in the scientific literature and, then, to plot speed profiles to illustrate complete driver speed behaviour on two-lane rural roads individualizing critical roadway sections where the speed differences, between road geometric components, are inappropriate.
Safety Performance Functions for Low-Volume Roads
The Baltic Journal of Road and Bridge Engineering, 2011
This paper analyzes roadway safety conditions using the network approach for a number of Italian roadways within the Province of Salerno. These roadways are characterized by low-volume conditions with a traffic flow of under 1000 vpd and they are situated partly on flat/rolling terrain covering 231.98 km and partly on mountainous terrain for 751.60 km. Since 2003, the Department of Transportation Engineering at the University of Naples has been conducting a large-scale research program based on crash data collected in Southern Italy. The research study presented here has been used to calibrate crash prediction models (CPMs) per kilometer per year. The coefficients of the CPMs are estimated using a non-linear multi-variable regression analysis utilizing the least-square method. In conclusion, two injurious crash prediction models were performed for two-lane rural roads located on flat/rolling area with a vertical grade of less than 6% and on mountainous terrain with a vertical grade of more than 6%. A residuals analysis was subsequently developed to assess the adjusted coefficient of determination and p-value for each assessable coefficient of the prediction model. CPMs are a useful tool for estimating the expected number of crashes occurring within the roads' geometric components (intersections and road sections) as a function of infrastructural, environmental, and roadway features. Several procedures exist in the scientific literature to predict the number of crashes per kilometer per year. CPMs can also be used as a tool for safety improvement project prioritization.
Preliminary Canter of the Accident Rate in Italian and Lithuanian Road Networks
The 9th International Conference "Environmental Engineering 2014", 2014
One of the major tasks in developing transport systems is to decrease human losses caused by traffic accidents. The social consequences of accidents and the relating human losses were the main reason why the European Ministers of Transport decided to take measurements in 2002 and then in 2010 in order to decrease the number of traffic accident related deaths in Member States Italian and Lithuanian researchers have developed national statistical methods to answer to European directives in their own country. The aim of this research study is to investigate the safety conditions of Italian and Lithuania two-lane rural roads in order to figure out why accidents occur and by what means they can be avoided. Current safety situation needs to be known for selecting locations to be treated as well as for evaluating the effects. In fact the deaths of persons and serious economic loss caused by road crashes demand a continuous attention in accordance with the rapid population growth and increasing economic activities that have resulted in many European cities. A study period of 5 years of the accident database was investigated in order to estimate the hazard conditions for each road segment by comparing the number of crashes over a specific roadway segment over a specific time period with statistically thresholds. In this way, by knowing critical road segments, it makes to define a consistent combination of interventions according also to the difference between design and operating speed value reducing accident frequency, its severity and social cost for the more frequently expected and dangerous accident scenario. Further investigations on accident types in defined most hazardous road segments, i.e. where calculated severe crash rate, must be analyzed and selected road safety measures for improving safety situation at a site.
Freeway Safety Management: Case Studies in Italy
TRANSPORT, 2012
Road safety has since become one of the major factors for a description of the state traffic system and crashes are often due to bad made decisions by drivers in environments created by engineers. This study proposes an update of the previous version (Dell'Acqua et al. 2011a) to estimate V 85, for non-conditioned traffic flows on freeways. The databases used in the study come from a series of speed measurements and vehicle ranges on a stretch of freeway using a fixed measuring system. The produced model proved to be very reliable, with the greatest error in the estimation of V 85, being less than 6%. The model obtained was then applied to a stretch of freeway of approximately 20 km. Some significant correlations between DV 85 (variation of V 85 among successive stretches) and DN (the variation in the number of crashes among successive stretches) were found, which may be very useful in the management of safety on roads. In particular the obtained results have highlighted some asp...
Development of safety performance functions for Spanish two-lane rural highways on flat terrain
Over decades safety performance functions (SPF) have been developed as a tool for traffic safety in order to estimate the number of crashes in a specific road section. Despite the steady progression of methodologi-cal innovations in the crash analysis field, many fundamental issues have not been completely addressed. For instance: Is it better to use parsimonious or fully specified models? How should the goodness-of-fit of the models be assessed? Is it better to use a general model for the entire sample or specific models based on sample stratifications? This paper investigates the above issues by means of several SPFs developed using negative binomial regression models for two-lane rural highways in Spain. The models were based on crash data gathered over a 5-year period, using a broad number of explanatory variables related to exposure , geometry, design consistency and roadside features. Results show that the principle of parsimony could be too restrictive and that it provided simplistic models. Most previous studies apply conventional measurements (i.e., R 2 , BIC, AIC, etc.) to assess the goodness-of-fit of models. Seldom do studies apply cumulative residual (CURE) analysis as a tool for model evaluation. This paper shows that CURE plots are essential tools for calibrating SPF, while also providing information for possible sample stratification. Previous authors suggest that sample segmentation increases the model accuracy. The results presented here confirm that finding, and show that the number of significant variables in the final models increases with sample stratification. This paper point out that fully models based on sample segmentation and on CURE may provide more useful insights about traffic crashes than general parsimonious models when developing SPF.
Transportation Research Part F: Traffic Psychology and Behaviour, 2014
Speeds are affected by several variables such as driver characteristics, vehicle performance, road geometrics, environmental conditions and driving regulations. It is therefore important to study the relationships between speed and such variables in order to facilitate conscious speed management on existing and planned roads, and to induce drivers to select a speed consistent with the posted limit. This relationship is of great interest to those who wish to achieve roadway functionality and improve overall road safety.