Salvatore Antonio Biancardo

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.

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.

Operating speed as a key factor in studying the driver behaviour in a rural context

The research aims to explore the effects of geometric road features on driver speed behaviour in order to identify unsafe road segments where high reductions in speed between successive road elements occur. The sample involves two-lane rural roads on flat terrain (vertical grade less than 5%) in Southern Italy, totalling 184 km without spiral transition curves between the tangent segments and circular elements. The testing was carried out on 567 study sites, of which 248 are on circular curves and 319 on tangents. Speed data collection was carried out in environmental and traffic conditions using a laser. The conditions were the following: dry roads, free flow conditions, daylight hours and good weather conditions. The main goal was to calibrate and validate different operating speed prediction models: a) one model on tangent segments; b) one model on circular curves; c) only one model to be used at the same time on tangents and circular curves. The validation process involved almost 10% of the total road network length, that was removed from the calibration phase. The speed measurements of each of the first two datasets (a, b) were grouped into ten homogeneous substrates while for the remaining dataset (c) sixteen substrates were defined by using a hard c-means algorithm. Two statistical criteria were used to remove anomalous operating speed values from each group of three datasets, namely, the Chauvenet criterion and the Vivatrat method. The first criterion was preferred in the final process of model selection. The results of the first filtering procedure showed more homogeneous samples that guaranteed a higher correlation coefficient and lower residuals of the predictive models during the validation phase than the Vivatrat method. The models were developed using an Ordinary Least Squares (OLS) method. The explanatory variables were total segment length, lane width, curvature of the road element, the curvature change rate on homogeneous road segments, and the number of residential driveways per km. ANOVA and additional synthetic statistical parameters were assessed to check the effectiveness of using a single general model to predict operating speeds at the same time on tangents and on circular curves alike. The results suggested the reliability of this hypothesis and its effectiveness in bringing advantages during the application phase.

Driver speed behavior on circular curves of undivided two-lane rural roads

Many researchers have proved that one of the parameters that most influence safe driving is operating speed, defined as the speed at which drivers travel on a dry road in free-flow conditions during daylight hours. This study describes a comparison between driver speed behavior on horizontal circular curves located in Serbia and on circular curves located in Italy with analogous infrastructural–geometric contexts. Only curves within a two-lane rural road network in low-volume conditions without spiral transition curves were studied. Operating speed profiles were plotted, and a careful analysis was carried out to study the deceleration and acceleration motion at each selected curve. Speed measurements were conducted by using different devices: (a) for the Italian case, laser detectors were used that emit and receive a pair of laser beams perpendicular to the roads axis and record the instantaneous vehicle speed and (b) for the Serbian case, sensors were used with an application software developed in the LabVIEW programming environment, and signals were logged in a binary file for which the vehicle speed was measured on the basis of the passing time between two consecutive sensors along the curve. Two types of driver behavior were found: (a) deceleration when approaching the middle of the curve and acceleration leaving it and (b) deceleration over the whole circular curve length. A t-test was performed. Statistically, no significant difference was produced from the analysis when circular curves were located in different states that reflected the same geometric, traffic, and environmental context.

A statistical look at gender and age differences as related to the injury crash type on low-volume roads

The research presented here is addressed to develop only one safety performance function (SPF) from the perspective of driver gender for three identified main crash types (head-on/side collisions, rear-end collisions, single-vehicle run-offroad crashes) that is able to predict the injury crash rate on low-volume roads. According to the police crash reports, it became evident that males and females differ in terms of their psychological attributes and, consequently, their response to the crash risk can change producing different effects on the severity. The analysis was divided into two phases: the first deals with SPF calibration, while the second concerns SPF validation. A total length of 355 km was used in the first phase involving 5 years of the crash database (2003–2007), to a total of 95 injury crashes which led to 136 injuries (63% male only drivers, 8% female only drivers and 29% female+male drivers) and 9 deaths (78% male only drivers and 22% female+male drivers). A total length of 295 km was used in the second phase involving 3 years of the crash database (2008–2010), to a total of 73 injury crashes which led to 120 injuries (68% male only drivers, 4% female only drivers and 28% female+male drivers) and 4 deaths (75% male only drivers and 25% female+male drivers). GEE was adopted to calibrate SPF. Mean width, mean speed at each analyzed road segment, and a numerical variable “SLEH” 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 happened, were introduced in the predictive safety model looking toward gender and age drivers.

Assessing Transferability of Highway Safety Manual Crash Prediction Models to Data from Italy

For decades, crashes have been studied as discrete events with the focus on the circumstances of the crash. This type of analysis has been used to identify the characteristics of roadway features associated with higher crash experience, but other factors, such as traffic volumes, driver characteristics, land use, and environmental conditions, are also needed to explain or describe crash events. The Highway Safety Manual (HSM) provides a predictive method to estimate the expected average crash frequency of a site in given geometric and geographic conditions over a specific period for a specific annual average daily traffic volume. The study presented here investigated whether the modeling results closely matched the crash records. The HSM algorithms were used to assess transferability as a whole. The results suggested that implementing the HSM techniques should foster the development of local safety performance functions and accident modification factors. Calibration preserved the original HSM model form and the relationship between independent variables and crashes. To adjust the base predicted crash frequency to meet the current conditions, the accident modification factor calculations for lane width, horizontal curves, and vertical grades were made. Crash types (head-on and side collisions, single-vehicle crashes, and rear-end collisions) were investigated on the basis of the vertical grade and the curvature indicator. The estimated model provides planners and designers with a tool better able to target and select countermeasures to address these specific aspects and results in improved project selection and improved safety.