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A brief overview of pedestrian accident modelling
No full textThanks to the advancement of models and methodologies to handle large amounts of data in conjunction with the increased attention of national and international governments toward safety issues, this paper aims to provide a brief overview of the main models that are used to study pedestrian crashes. The reason why this type of analysis is conducted starts from three main research questions: What are the main datasets needed to study pedestrian accidents, what are the main models utilized, and what are the main gaps that emerge in the pedestrian safety field? This proposed state-of-the-art overview starts from the analysis of statistical approaches in the context of risk factor analysis to the most recent machine learning methods to evaluate pedestrian crash severity by emphasizing the purposes for which the models are used, why they are used, and the data needed to achieve the task. The results of the analysis show how the models could be classified and the main research gaps in this field that could be useful for researchers as starting points in their studies
Interpretable crash severity prediction models to improve cyclist safety
No full textThis study focuses on 3 years (2016–2018) of cyclist crashes in the City of Rome, Italy. As the first step, a statistical analysis was carried out. Several Cycling Crash Models were developed by using Logistic Regression Models, with a deep dive into the most influencing variables. The two proposed models at intersections and single-lane carriageways have a McFadden score or pseudo-R2 of 0.3976809 and 0.4495008, respectively. The findings show that visibility does not play a key role in leading to a crash with a cyclist; sunny weather is positively correlated to crashes in intersections, while dry surfaces increase the chances of having crashes on single-lane carriageways, such as also the location of these roads in extra-urban environments and autumn and winter seasons. Weekdays are also related to an increase in the probability of having a crash at intersections and on single-lane carriageways. Cyclist crashes are more likely to happen in the evening and nighttime hours. Vertical and horizontal signposting decreases the probability of crashes in intersections and single-lane carriageways. High values of average daily traffic (>2000 vehicles/day) are strongly related to crashes on single-lane carriageways, and high speeds (>50 km/h) increase the probability of fatal crashes in intersections and on single-lane carriageways
On the expressivity of the ExSpliNet KAN model
No full textExSpliNet is a neural network model that combines ideas of Kolmogorov networks, ensembles of probabilistic trees, and multivariate B-spline representations. In this paper, we study the expressivity of the ExSpliNet model and present two constructive approximation results that mitigate the curse of dimensionality. More precisely, we prove new error bounds for the ExSpliNet approximation of a subset of multivariate continuous functions and also of multivariate generalized bandlimited functions. The main ingredients of the proofs are a constructive version of the Kolmogorov superposition theorem, Maurey's theorem, and spline approximation results. The curse of dimensionality is lessened in the first case, while it is completely overcome in the second case. Since the considered ExSpliNet model can be regarded as a particular version of the recently introduced neural network architecture called Kolmogorov-Arnold network (KAN), our results also provide insights into the analysis of the expressivity of KANs
Desert cyanobacteria under non-Earth conditions: Implications for astrobiology and sustainable life support
Full text linkFrom the Manichean Dichotomy, Through the Biopsychosocial Model, to Systems Sexology, the Final Evolution of Sexual Medicine
Full text linkPedestrian crash severity prediction and contributory factors analysis by using machine learning methods
No full textPedestrians occupy a leading position among the most vulnerable road users. Each year about 270,000 pedestrians die due to road accidents, so this study aims to highlight the most influencing contributory factors and the most promising models to predict pedestrian crash severity. ISTAT data for the City of Rome (2013–2020) are used and different Machine Learning Methods are trained and tested, after balancing the data with oversampling techniques. In addition, analysis of the most influencing contributory factor is carried out, by using the ROC curve method, Variable Importance Analysis (VIP), and Support Vector Machine with a Linear Kernel. The findings suggest that the model with the best prediction performance is the Random Forest, followed by the Decision Tree and k-nearest neighbour algorithm. Regarding the analysis of contributory factors, the methods implemented highlight that the hour in which the accident occurs, pedestrian gender, and age seem to be the most critical factors that increase the severity of a pedestrian crash. There are also some limitations in this study: the first is connected to the black-box nature of these models; the second regards how these variables could influence positively or negatively the outcome
Quadrature rules for splines of high smoothness on uniformly refined triangles
No full textIn this paper, we identify families of quadrature rules that are exact for sufficiently smooth spline spaces on uniformly refined triangles in R2. Given any symmetric quadrature rule on a triangle T that is exact for polynomials of a specific degree d, we investigate if it remains exact for sufficiently smooth splines of the same degree d defined on the Clough-Tocher 3-split or the (uniform) Powell-Sabin 6-split of T. We show that this is always true for C(2r-1) splines having degree d = 3r on the former split or d = 2r on the latter split, for any positive integer r. Our analysis is based on the representation of the considered spline spaces in terms of suitable simplex splines