Gianluca Dell’Acqua

Risk-type density diagrams by crash type on two-lane rural roads

The research presented here aims to plot density diagrams per road crash risk type to identify all possible scenarios where driving is less than safe. The starting point was the prediction of injury crash rate on horizontal homogeneous segments of two-lane rural roads for three main injurious crash types (head-on/side collisions, rear-end crashes, and single-vehicle run-off-road crashes) as observed on the network. A careful analysis of the database shows that a wide variety of factors appear to be influenced or associated with the crash dynamic, as follows: the road scenario (combination of infrastructure and environmental conditions found at the site at the time of the crash), mean lane width, the horizontal curvature indicator (measurement of the curvature change rate), and mean speed. Crashes recorded from 2003 to 2010, of which 1597 were injurious, and 645 resulted only in damage to property, were analyzed on more than 3700 km of road network in Southern Italy. Generalized estimating equations with a negative binomial distribution were implemented. Risk-type density charts were plotted to thoroughly identify all possible combinations of existing explicative variables producing hazardous conditions on the road. The different shades in the diagrams represent different ranges of injurious crash rates: the white band shows low levels, while a black band shows high values. It is not possible to consider working on an explanatory variable to reduce hazardous conditions on the road network without also considering how this variation might affect the influence of the remaining explanatory variables on crash phenomena and, consequently, on the predictive model. The risk maps make it possible to keep under control in a simple and immediate approach the way each variable as a result of variations of a part or of all.

Aircraft safety analysis using clustering algorithms

In recent years, there have been many cost-benefit studies on aviation safety, which deal mainly with economic issues, omitting some strictly technical aspects. This study compares aircraft accidents in relation to the characteristics of the aircraft, environmental conditions, route, and traffic type. The study was conducted using a database of over 1500 aircraft accidents worldwide, occurring between 1985 and 2010. The data were processed and then aggregated into groups, using cluster analysis based on an algorithm of partition binary ‘Hard c means.’ For each cluster, the ‘cluster representative’ accident was identified as the average of all the different characteristics of the accident. Moreover, a ‘hazard index’ was defined for each cluster (according to annual movements); using this index, it was possible to establish the dangerousness of each ‘cluster’ in terms of aviation accidents. Obtained results allowed the construction of an easy-to-use predictive model for accidents using multivariate analysis.

Road Safety from the Perspective of Driver Gender and Age as Related to the Injury Crash Frequency and Road Scenario

Objective: The objective of this research is to develop safety performance functions (SPFs) on 2-lane rural roads to predict the number of injury crashes per year per 108 vehicles/km on the road segment using a study on the influence of the human factors (gender, age, number of drivers) and road scenario (combination of infrastructure and environmental conditions found at the site at the time of the crash) on the effects of a crash by varying the dynamic. Countermeasures are suggested to reduce the injury crash rate and include different awareness campaigns and structural measures on the segments of road. Methods: An 8-year period was analyzed for which 5 years of crash information were used to calibrate and specify SPFs and the remaining 3 years were used to check the reliability of the equations. Before moving to the calibration phase, a technique to filter anomalous injury crash rates was adopted by using a method widely used in geotechnical engineering that is based on estimates of ranges of values that can be considered fluctuations of the “regular” measures compared to values estimated as “abnormal” for each homogeneous scenario. Due to overdispersion of crash data, generalized estimating equations and additional log linkage equation were adopted to calibrate SPFs. The Akaike information criterion and Bayesian information criterion were used to check the reliability of the models. Results: Six SPFs were calibrated: for head-on/side collisions, one equation was built for circular curves and one for tangent segments; for rear-end collisions, one equation was built for daylight and one for the hours of darkness; for single-vehicle run-off-road crashes, one equation was built for wet road surface conditions and one for dry road surface conditions. An original numerical variable, SLEH, was designed to calibrate safety models reflecting the identified road surface (dry/wet), light conditions (day/night), geometric element (tangent segment/circular curve), and human factors (gender/age/number drivers) all together when the crash occurred, as provided by related police reports. The validation procedure succeeded. It emerged that males and females are involved in crashes of varying degrees of frequency, depending on the driving scenario that presents itself and the gender of the other drivers involved in the crash. Several different dangerous scenarios were identified: only female drivers on a dry road surface in daylight on tangent segments increased the risk for head-on/side collisions; only male drivers on a wet road surface in daylight on circular curves increased the risk for single-vehicle crashes; and crashes involving both female and male drivers on a dry road surface in daylight on a circular curve increased the risk for head-on/side collisions. Conclusion: According to the current study, based on the network approach for the allocation of economic resources and planning of road safety strategies, calibration of injury crash rate prediction models for specific target collision type is important because of the range of harms that are caused by different collision types. From these studies it is apparent that the age and gender of drivers considered together further refines how those factors contribute to crashes. Countermeasures (structural road interventions and/or safety awareness campaigns) can be planned to reduce the highest rate of injury crash for each gender and road scenario: the awareness campaigns cannot be generalized or vague but must be organized by age and gender, because this study shows that crash dynamics alter as these factors change, with consideration for the varying psychological traits of the driver groups. Before-and-after safety evaluations can be used to check the safety benefits of improvements carried out on the roadways, within budget constraints for improvement or safety compliance investments for future operation. Supplemental materials are available for this article. Go to the publishers online edition of Traffic Injury Prevention to view the supplemental file.

Modeling driver behavior by using the speed environment for two-lane rural roads

Roadway safety involves the three components of the roadway system: the people, the vehicle, and the roadway. When the design corresponds to what the driver hopes to find, the road is consistent; the most widely used methods to evaluate design consistency are based on analysis of the operating speed profile. When the roadway alignment does not violate driver expectations, the possibility of drivers making errors is reduced. The aim of this experimental study is to obtain widely valid formulas during the design phase to predict the speeds that will actually be found on rural roads. Studies have shown that the dominant influences on curve speed are local curvature and the speed environment pertaining to the road section. The environment speed is a drivers preferred speed of travel within a road section with relatively homogeneous alignment. For the purposes of this study, this speed was taken as the 85th percentile speed measured on a tangent or on the curve with the highest radius in a homogeneous road section.

Field measurements on runway friction decay related to rubber deposits

Surfaces of airport pavements are subject to contamination that can be very dangerous for the movement of aircraft particularly on the runway. A recurrent problem is represented by the deposits of vulcanized rubber of aircraft tires in the touchdown area during landings and lesser during take-offs. This causes a loss of grip that compromises the safety of aircraft movements in take-off and landing operations. This study deals with the surface characteristics decay phenomenon related to contamination from rubber deposits. The experiment was conducted by correlating the pavement surface characteristics, as detected by Grip Tester, to air traffic before and after de-rubberizing operation and two models were constructed for the assessment of functional capacity of the runway before and after the operations de-rubberizing.

Using artificial neural network and multivariate analysis techniques to evaluate road operating conditions

Regional paved roads are low volume roads with a prevalence of heavy traffic. In the world, these roads concern about 80% of the total road network; however, the traffic that affects these roads is about 20%. Since regional roads are characterized by weak demand, budget for their management/maintenance is very low. This produces considerable difficulties in the choice of strategies for maintenance planning and scheduling. For this reason, the recurring topics of research in this field deal with typical roads issues and aim to develop low cost tools and methods. The study proposes a decision support system to evaluate regional paved roads operating condition in relation to the hydrogeological situation. In particular, the system allows to evaluate in a quick and easy manner, the operating conditions of the road, through low-cost tools (i.e. using low economic resources). This is very useful in the case of LVRs because administrations for these roads have a limited budget. The procedure is developed on a regional paved roads network based on more than 80 roads located in Southern Italy. Data is collected by direct surveys in the field and is integrated with cartography and information available in road agency records. From data analysis, obtained using two different techniques, an easy and quick use procedure is made. In particular, Model 1 is built through multivariate analysis and Model 2 using the artificial neural network (ANN) technique. The results show the validity of the two models in Regional paved roads operating conditions estimation in relation to hydrogeological situations of sites. Both models show good reliability. In particular, the first model (Model 1) is characterized by a high level of significance (p < 0.01) and by a coefficient of determination equal to 0.82. Comparative tests between the second model (Model 2) on which standard tests cannot be performed for obvious reasons, and the first model (Model 1). The results show that the ANN model (model 2), characterized by lower residual, simulates more accurately than the second (Model 1).