Road functional classification using pattern recognition techniques

The existing international standards suggest a methodology to assign a specific functional class to a road, by the values of some features, both geometrical and use-related. Sometimes, these characteristics are in contrast with each other and direct the analyst towards conflicting classes for a road or, worse, one or more of these features vary heterogeneously along the road. In these conditions, the analyst assigns the class that, by his capability and experience, he retains the most appropriate, in a very subjective way. On the contrary, the definition of an automatic procedure assuring an objective identification of the most appropriate functional class for each road would be desirable. Such a solution would be useful, especially when the road belongs to the existing infrastructure network or when it was not realised by out of date standards. The proposed procedure regards the definition of a classification model based on Pattern Recognition techniques, considering 13 input variables that, depending on their assumed value, direct the analyst towards one of the four functional classes defined by the Italian standards. In this way, it is possible to classify a road even when its characteristics are heterogeneous and conflicting. Moreover, the authors analysed the model limitations, in terms of errors and dataset size, considering observation and variable numbers. This approach, representing a beneficial decision support tool for the decision-maker, is exploitable for both planned and existing roads and becomes particularly advantageous for road agencies aiming to optimally allocate their limited funds for specific interventions assuring the achievement of a fixed functional class

Analysis of different visual strategies of ‘isolated vehicle’and ‘disturbed vehicle’

This paper analyses the driver’ visual behaviour in the different conditions of ‘isolated vehicle’ and ‘disturbed vehicle’. If the meaning of the former is clear, the latter condition considers the influence on the driving behaviour of various objects that could be encountered along the road. These can be classified in static (signage, stationary vehicles at the roadside, etc.) and dynamic objects (cars, motorcycles, bicycles). The aim of this paper is to propose a proper analysis regarding the driver’s visual behaviour. In particular, the authors examined the quality of the visually information acquired from the entire road environment, useful for detecting any critical safety condition. In order to guarantee a deep examination of the various possible behaviours, the authors combined the several test outcomes with other variables related to the road geometry and with the dynamic variables involved while driving. The results of this study are very interesting. As expected, they obviously confirmed better performances for the ‘isolated vehicle’ in a rural two-lane road with different traffic flows. Moreover, analysing the various scenarios in the disturbed condition, the proposed indices allow the authors to quantitatively describe the different influence on the visual field and effects on the visual behaviour, favouring critical analysis of the road characteristics. Potential applications of these results may contribute to improve the choice of the best maintenance strategies for a road, to select the optimal signage location, to define forecasting models for the driving behaviour and to develop useful instruments for intelligent transportation systems

How is the Driver's Workload Influenced by the Road Environment?

This paper focuses on the study of the driver\u2019s workload while driving on a rural two-lane road with different traffic flows. The aim of the research is to examine a parameter that could be representative of the driving effort, quite sensible to the external factors that cause disturbance to the regular driving activity. To solve this problem, the authors used a specific instrumented vehicle for monitoring some physiological parameters of the driver (as the eye movements and the Galvanic Skin Resistance), referring their values to the road context. The results are very interesting and confirm that knowing the workload is useful to improve the road safety only if it is related to the external context, as well as road geometry, traffic, visibility, etc. Only in this way, the road administrators can deduce proper information to plan and direct accurate and productive upgrade working operation

On the Advent of Super-Resolution Microscopy in the Realm of Polycomb Proteins

Simple Summary The genomes of metazoans are organized at multiple spatial scales, ranging from the double helix of DNA to whole chromosomes. The intermediate genomic scale of kilobases to megabases, which corresponds to the 50-300 nm spatial scale, is particularly interesting because the tridimensional arrangement of chromatin is implicated in multiple regulatory mechanisms. Indeed, a crucial hallmark of cellular life is the widespread ordering of many biological processes in nano-/mesoscopic domains (10-200 nm), which now may be revealed by an imaging toolbox referred to as super-resolution microscopy. In this context, polycomb proteins stand as major epigenetic modulators of chromatin function, acting prevalently as repressors of gene transcription. This work reviews the current state-of-the-art super-resolution microscopy applied to polycomb proteins. Of note, super-resolution data have complemented cutting-edge molecular biology methods in providing a rational framework for understanding how polycomb proteins may shape 3D chromatin topologies and functions. The genomes of metazoans are organized at multiple spatial scales, ranging from the double helix of DNA to whole chromosomes. The intermediate genomic scale of kilobases to megabases, which corresponds to the 50-300 nm spatial scale, is particularly interesting, as the 3D arrangement of chromatin is implicated in multiple regulatory mechanisms. In this context, polycomb group (PcG) proteins stand as major epigenetic modulators of chromatin function, acting prevalently as repressors of gene transcription by combining chemical modifications of target histones with physical crosslinking of distal genomic regions and phase separation. The recent development of super-resolution microscopy (SRM) has strongly contributed to improving our comprehension of several aspects of nano-/mesoscale (10-200 nm) chromatin domains. Here, we review the current state-of-the-art SRM applied to PcG proteins, showing that the application of SRM to PcG activity and organization is still quite limited and mainly focused on the 3D assembly of PcG-controlled genomic loci. In this context, SRM approaches have mostly been applied to multilabel fluorescence in situ hybridization (FISH). However, SRM data have complemented the maps obtained from chromosome capture experiments and have opened a new window to observe how 3D chromatin topology is modulated by PcGs

A PSO algorithm for designing 3D highway alignments adopting polynomial solutions

Intelligent optimization algorithms for highway alignments have produced good results so far. However, considering the numerous constraints and factors directly implied in the infrastructure design, the researchers' efforts usually focus only on simplifying the alignment choice, supporting engineers in the design phase. Implementing strategic considerations regarding comfort and safety would be also very important. In this paper, the authors propose a method for designing improved 3D highway alignments using a specific optimization algorithm, based on a Swarm Intelligence technique, adopting an innovative polynomial transition curve as the unique horizontal curvature element, called PPC (Polynomial Parametric Curve). This geometric solution assures higher levels of comforts for users than the traditional ones (clothoid—circular curve—clothoid), because the PPC shows more gradual trends of the main dynamic variables involved while driving, and defines each whole curve through a unique element, simplifying the design procedure. The authors provide technical and operational details for improving a Swarm optimization model through the adoption of the PPC and prove the efficacy of the proposed procedure through a specific significant exampl

An ANN model to correlate roughness and structural performance in asphalt pavements

In this paper, using a large database from the Long Term Pavement Performance program, the authors developed an Artificial Neural Network (ANN) to estimate the structural performance of asphalt pave- ments from roughness data. Considering advantages of modern high-performance survey devices in the acquisition of road pavement functional parameters, it would be of practical significance if the struc- tural state of a pavement could be estimated from its functional conditions. To differentiate various road section conditions, several significant input parameters, related to traffic, weather, and structural aspects, have been included in the analysis. The results are very interesting and prove that the ANN represents an adequate model to evidence this relation. The papers shows the effectiveness of the adoption of a large database for the analysis of the correlation. ANN provides also better results in comparison with Linear Regression. Further, the authors trained three different ANNs to analyse the effects of modified datasets and different variables. The numerical outcomes confirm that, by using this approach, it is possible to cor- relate with good accuracy roughness and structural performance, allowing road agencies to actually reduce the deflection test frequency, since they are generally more costly, time consuming, and disrup- tive to traffic than functional surveys

Using Genetic Algorithms for Optimizing the PPC in the Highway Horizontal Alignment Design

Various studies have emphasized the interesting advantages related to the use of new transition curves for improving the geometric design of highway horizontal alignments. In a previous paper, one of the writers proposed a polynomial curve, called a polynomial parametric curve (PPC), proving its efficiency in solving several design problems characterized by a very complex geometry (egg-shaped transition, transition between reversing circular curves, semidirect and inner-loop connections, and so on). The PPC also showed considerable advantages from a dynamic perspective, as evidenced by the analysis of the main dynamic variables related to motion (as well as rate of change of radial acceleration, steering speed, roll speed, and so on). In this paper, an optimization procedure using genetic algorithms (GAs) for selecting the different parameters of the PPC has been proposed. In particular, a specific algorithm defines the parameter values in order to minimize an appropriate fitness function. Besides, the final PPC can be examined from a dynamic point of view for evaluating the compliance with the comfort and safety conditions. Moreover, to simplify the geometric representation and the calculation of the dynamic variables of the PPC, using computer software, a specific and innovative routine has been specifically developed by the writers

Road design problems solved by affine arithmetic

In this paper we propose the use of techniques based on range numbers for solving problems of road designing. We applied the Affine Arithmetic to verify the driver's visibility in a horizontal curve, providing very interesting results and confirming its usefulness even in the most complex analytical expressions. The preparation of charts such as those proposed in this paper also makes this method directly accessible to those not so familiar with this technique