Antonio D'Ambrosio

Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery.

Aim of the study was the analysis of powered two-wheeler (PTW) crashes in Italy in order to detect interdependence as well as dissimilarities among crash characteristics and provide insights for the development of safety improvement strategies focused on PTWs. At this aim, data mining techniques were used to analyze the data relative to the 254,575 crashes involving PTWs occurred in Italy in the period 2006-2008. Classification trees analysis and rules discovery were performed. Tree-based methods are non-linear and non-parametric data mining tools for supervised classification and regression problems. They do not require a priori probabilistic knowledge about the phenomena under studying and consider conditional interactions among input data. Rules discovery is the identification of sets of items (i.e., crash patterns) that occur together in a given event (i.e., a crash in our study) more often than they would if they were independent of each other. Thus, the method can detect interdependence among crash characteristics. Due to the large number of patterns considered, both methods suffer from an extreme risk of finding patterns that appear due to chance alone. To overcome this problem, in our study we randomly split the sample data in two data sets and used well-established statistical practices to evaluate the statistical significance of the results. Both the classification trees and the rules discovery were effective in providing meaningful insights about PTW crash characteristics and their interdependencies. Even though in several cases different crash characteristics were highlighted, the results of the two the analysis methods were never contradictory. Furthermore, most of the findings of this study were consistent with the results of previous studies which used different analytical techniques, such as probabilistic models of crash injury severity. Basing on the analysis results, engineering countermeasures and policy initiatives to reduce PTW injuries and fatalities were singled out. The simultaneous use of classification trees and association discovery must not, however, be seen as an attempt to supplant other techniques, but as a complementary method which can be integrated into other safety analyses.

Perceptual Measures to Influence Operating Speeds and Reduce Crashes at Rural Intersections: Driving Simulator Experiment

The aim of this paper is to investigate, by means of a dynamic driving simulator experiment, the behavior of road users at rural intersections in relation to perceptual measures designed for increasing hazard detection. In the experiment 10 configurations of tangents were tested: Alt1, base tangent; Alt2, four-leg base intersection; Alt3, intersection with reduced sight distance; and Alt4 through Alt10, intersections with perceptual treatments. The Virtual Environment for Road Safety high-fidelity dynamic-driving simulator, operating at the Technology Environment Safety Transport Road Safety Laboratory located in Naples, Italy, was used. Analysis of the results used two approaches: (a) explorative description of data by cluster analysis and (b) inferential procedures about population using statistical tests. Results showed that the speed behavior in the tangents was significantly affected by the presence of the intersections and by the perceptual treatments. Intersections without perceptual treatments significantly affected driver speeds in the 250 m preceding the intersection. Perceptual treatments helped the driver to detect the intersection earlier and to slow down. Dragon teeth markings, colored intersection area, and raised median island performed better than the other perceptual treatments. They produced significant average speed reduction in the 150 m preceding the intersection ranging between 16 km/h and 23 km/h. Study results support real-world implementation of perceptual measures in rural intersections because they are low-cost, fast implementation measures with a high potential to be cost-effective.

Robust tree-based incremental imputation method for data fusion

Data Fusion and Data Grafting are concerned with combining files and information coming from different sources. The problem is not to extract data from a single database, but to merge information collected from different sample surveys. The typical data fusion situation formed of two data samples, the former made up of a complete data matrix X relative to a first survey, and the latter Y which contains a certain number of missing variables. The aim is to complete the matrix Y beginning from the knowledge acquired from the X. Thus, the goal is the definition of the correlation structure which joins the two data matrices to be merged. In this paper, we provide an innovative methodology for Data Fusion based on an incremental imputation algorithm in tree-based models. In addition, we consider robust tree validation by boosting iterations. A relevant advantage of the proposed method is that it works for a mixed data structure including both numerical and categorical variables. As benchmarking methods we consider explicit methods such as standard trees and multiple regression as well as an implicit method based principal component analysis. A widely extended simulation study proves that the proposed method is more accurate than the other methods.

Parsimonious time series clustering using P-splines

A new parsimonious way to cluster time (data) series is provided.We deal with P-spline framework and non-hierarchical clustering.Simulation studies and two well-known real world case studies are performed. We introduce a parsimonious model-based framework for clustering time course data. In these applications the computational burden becomes often an issue due to the large number of available observations. The measured time series can also be very noisy and sparse and an appropriate model describing them can be hard to define. We propose to model the observed measurements by using P-spline smoothers and then to cluster the functional objects as summarized by the optimal spline coefficients. According to the characteristics of the observed measurements, our proposal can be combined with any suitable clustering method. In this paper we provide applications based on non-hierarchical clustering algorithms. We evaluate the accuracy and the efficiency of our proposal by simulations and by analyzing two real data examples.

A differential evolution algorithm for finding the median ranking under the Kemeny axiomatic approach

An accurate (meta)heuristic solution to the rank aggregation problem is proposed.The reference paradigm is the KemenySnell axiomatic framework.We specifically adapt the differential evolution algorithm to deal with the median ranking problem.Simulation studies and real data applications are performed. In recent years the analysis of preference rankings has become an increasingly important topic. One of the most important tasks in dealing with preference rankings is the identification of the median ranking, namely that ranking that best represents the preferences of a population of judges. This task is known with several alternative names, such as rank aggregation problem, consensus ranking problem, social choice problem. In this paper we propose a Differential Evolution algorithm for the Consensus Ranking detection (DECoR) within the Kemenys axiomatic framework. The algorithm works with full, partial and incomplete rankings. A simulation study shows that our proposal is particularly feasible when working with a very large number of objects to be ranked, because it is accurate and also faster than other proposals. Some applications on real data sets show the practical utility of our proposal in helping the users in taking decisions.