1,720,990 research outputs found
Safety performance functions for road intersections in the Friuli Venezia Giulia region
Full text linkThe aim of this paper was developing Safety Performance Functions (SPFs) of four different types of road intersections: unsignalized three-leg intersection, unsignalized four-leg intersection, signalized threeleg intersections, signalized four-leg intersection. The data on accidents and traffic volume of 28 intersections, which are under the jurisdiction of Friuli Venezia Giulia Strade S.p.A., were collected and analyzed. To model the empirical relation between crash frequency and traffic volume, a feed-forward artificial neural network (ANN), characterized by the hyperbolic tangent transfer function and one hidden layer, was used. SPFs of the analyzed road intersections have shown a slope reduction of the tangent line when annual average daily traffic increased on the main leg, namely, the road capacity is not able to meet the increase in the volume of traffic. As a result, this leads to a saturation that induces slowdowns with a lower probability of running into an accident
Effects of cognitive distraction on driver’s stopping behaviour: A virtual car driving simulator study
Full text linkDrivers are prone to distractions while driving, due to conversations they have with passengers on board, processing their thoughts or using their mobile phones. These distractions result in a mental workload that compromises driving safety and requires the implementation of risk compensatory behaviours. This study examines the effects of hands-free mobile phone conversations on young drivers' stopping manoeuvres when a pedestrian enters a zebra crossing. A cohort of seventy-eight university students, aged 20-30 years old, performed a driving task in a virtual urban environment, by means of a virtual car driving simulator. They formed a control and an experimental group, balanced on age and IQ level. The control group was left free to drive without any imposed cognitive task. The experimental group was asked to drive while making a phone call that was planned to diminish the amount of cognitive resources allocated to the driving experience. For both groups, the analyses focused on a specific moment, i.e., while a child suddenly entered a zebra crossing from a sidewalk. Throughout the simulation, the intensity of the participants’ actions on the brake pedal, accelerator, and steering wheel were recorded with a time step of 250 ms. Before the virtual driving experiment, each participant completed a questionnaire on his/her daily driving style, involvement in road accidents, and general mobile phone usage even while driving. A mixed two-way ANOVA with Group as a between-subject factor (1. Control Group; 2. Experimental Group) and Gender (1. Male drivers; 2. Female drivers) as a within-subject factor was performed on the driving parameters as dependent variables. The results showed the presence of a significant difference for distracted and non-distracted drivers with the absence of gender-related differences across the two groups. Participants engaged in a hands-free phone-call while driving assumed lower initial speeds as an element of risk compensation and took the first action to stop at shorter distances from the pedestrian crossing. This suggests a delayed perception of the presence of the pedestrian. In addition, the fluctuation in speed after the distracted driver had released the accelerator pedal reached a statistical significance compared to the control group. These findings suggest that the distraction induced by the use of the mobile phone through the earphones may adversely affect driving behaviour and raise significant safety concerns
Stiffness modulus and marshall parameters of hot mix asphalts: Laboratory data modeling by artificial neural networks characterized by cross-validation
Full text linkThe present paper discusses the analysis and modeling of laboratory data regarding the
mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of
artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and
bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ)
were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was
characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear
transfer function. The effectiveness of such an approach was verified in terms of the coeffcients of
correlation (R) and mean square errors; in particular, R values were within the range 0.965–0.919 in
the training phase and 0.881–0.834 in the CV testing phase, depending on the predicted parameters
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Numerical characterization of high modulus asphalt concrete containing RAP: A comparison among optimized shallow neural models
Full text linkKnowing the relationship between the stiffness modulus and the empirical mechanical characteristics of asphalt concrete, road engineers may predict the expected results of costly laboratory tests and save both time and financial resources in the mix design phase. In fact, such a model would make it possible to assess a priori whether the stiffness of a specific mixture, characterised in the laboratory only by the common Marshall test, is suitable for the level of service required by the road pavement under analysis. In this study, 54 Marshall test specimens of high modulus asphalt concrete were prepared and tested in the laboratory to determine an empirical relationship between the stiffness modulus and Marshall stability by means of shallow artificial neural networks. Part out of these mixtures was characterised by different types of bitumen (20/30 or 50/70 penetration grade) and percentages of used reclaimed asphalt (RAP at 20% or 30%); a polymer modified bitumen was used in the preparation of the remaining Marshall test specimens, which do not contain RAP. For the complex and laborious identification of the neural model hyperparameters, which define its architecture and algorithmic functioning, the Bayesian optimization approach has been adopted. Although the results of this methodology depend on the predefined hyperparameters variability ranges, it allows an unbiased definition of the optimal neural model characteristics to be performed by minimizing (or maximizing) a loss function. In this study, the mean square error on 5 validation folds was used as a loss function, in order to avoid a poor performance evaluation due to the small number of samples. In addition, 3 different neural training algorithms were applied to compare results and convergence times. The procedure presented in this study is a valuable guide for the development of predictive models of asphalt concretes' behaviour, even for different types of bitumen and aggregates considered here
A machine learning approach for the prediction of very thin wearing layers asphalt concretes volumetric properties and performance
No full textIn recent years, many researchers in the field of pavement engineering have worked with the aim of developing a model capable of predicting the mechanical behavior of a mixture starting from its composition's parameters. This has been done following two different approaches. The first involved the use of advanced constitutive laws based on the materials mechanics; the second, instead of being physically based, was data-driven. The present work belongs to this second context and aims to present, implement and apply a strategy to develop the optimal model for solving an assigned predictive problem. Specifically, a Machine Learning approach, a Feedforward Backpropagation Shallow Neural Network, was investigated. The objective was to correlate stiffness modulus, air voids and voids in the mineral aggregate to the mixture main composition's parameters identified in: bitumen content, particle size and a categorical variable distinguishing the bitumen type and production site. Since the maximum aggregate size is 10 mm, the sieves considered were of 10, 6.3, 2, 0.5 and 0.063-mm diameters. The present study focused on 92 variants of asphalt concretes for very thin road pavement wearing layers produced both in plant and in laboratory. Despite the wide variation ranges of each parameter considered, the optimal model returns fully satisfactory performance. The overall Pearson correlation coefficient is equal to 0.9490, also by virtue of the innovative algorithms implemented as k-fold Cross-Validation (CV) and Bayesian Optimization (BO). These algorithms have allowed on the one hand the improvement of the model's predictive performance making them more reliable and, on the other hand, the optimization of hyperparameters and architecture. The methodology developed can become an important reference in this field since it is independent from the specific predictive application. In this sense, it can help other researchers in the fine-tuning of neural models in the field of pavement engineering
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