1,721,263 research outputs found
Long-term-based road blackspot screening procedures by machine learning algorithms
Full text linkScreening procedures in road blackspot detection are essential tools for road authorities for quickly gathering insights on the safety level of each road site they manage. This paper suggests a road blackspot screening procedure for two-lane rural roads, relying on five different machine learning algorithms (MLAs) and real long-term traffic data. The network analyzed is the one managed by the Tuscany Region Road Administration, mainly composed of two-lane rural roads. An amount of 995 road sites, where at least one accident occurred in 2012-2016, have been labeled as "Accident Case". Accordingly, an equal number of sites where no accident occurred in the same period, have been randomly selected and labeled as "Non-Accident Case". Five different MLAs, namely Logistic Regression, Classification and Regression Tree, Random Forest, K-Nearest Neighbor, and Naïve Bayes, have been trained and validated. The output response of the MLAs, i.e., crash occurrence susceptibility, is a binary categorical variable. Therefore, such algorithms aim to classify a road site as likely safe ("Accident Case") or potentially susceptible to an accident occurrence ("Non-Accident Case") over five years. Finally, algorithms have been compared by a set of performance metrics, including precision, recall, F1-score, overall accuracy, confusion matrix, and the Area Under the Receiver Operating Characteristic. Outcomes show that the Random Forest outperforms the other MLAs with an overall accuracy of 73.53%. Furthermore, all the MLAs do not show overfitting issues. Road authorities could consider MLAs to draw up a priority list of on-site inspections and maintenance interventions
Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms
No full textCrash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact that crash datasets are acutely imbalanced. Overlooking this fact generally leads to weak classifiers for predicting the minority class (crashes with higher severity). In this paper, in order to handle imbalanced accident datasets and provide a better prediction for the minority class, the random undersampling the majority class (RUMC) technique is used. By employing an imbalanced and a RUMC-based balanced training set, we propose the calibration, validation, and evaluation of four different crash severity predictive models, including random tree, k-nearest neighbor, logistic regression, and random forest. Accuracy, true positive rate (recall), false positive rate, true negative rate, precision, F1-score, and the confusion matrix have been calculated to assess the performance. Outcomes show that RUMC-based models provide an enhancement in the reliability of the classifiers for detecting fatal crashes and those causing injury. Indeed, in imbalanced models, the true positive rate for predicting fatal crashes and those causing injury spans from 0% (logistic regression) to 18.3% (k-nearest neighbor), while for the RUMC-based models, it spans from 52.5% (RUMC-based logistic regression) to 57.2% (RUMC-based k-nearest neighbor). Organizations and decision-makers could make use of RUMC and machine learning algorithms in predicting the severity of a crash occurrence, managing the present, and planning the future of their works.A
Experimental study on the performance properties of bio-based polymer modified binder
No full textIn the present work Tall Oil Pitch (TOP) and SBS were added to a plain binder 30/45 to meet the penetration and softening temperature of a reference polymer modified binder 25/55-55. Performance tests using the Dynamic Shear Rheometer (DSR) and the Bending Beam Rheometer (BBR) were carried out to see if the Bio polymer modified binder can reach similar performances in the high, intermediate and low temperature range to the reference polymer modified binder. The results showed that the chosen formulation can achieve similar rutting and thermal cracking performances, but not similar fatigue performances to the reference binder. Moreover, the effect of adding 50% of RAP binder to the Bio polymer modified binder were investigated. The results indicate better rutting performance and worst fatigue and thermal cracking resistance in comparison to the Bio polymer modified binder
A SWOT analysis of innovative high sustainability pavement surfaces containing crumb rubber modifier
No full textIn order to compare some innovative solutions of sustainable pavement surfaces containing crumb rubber modifier (CRM), a strengths, weaknesses, opportunities, and threats (SWOT) analysis of different technologies developed for production of low noise pavements (LNPs) has been carried out. CRM is one of the by-products obtained from end-of-life tyres (EOLT) that is currently recycled in asphalt pavements worldwide, with different technologies and more interest in some countries compared to others. In order to demonstrate the effective use of CRM in LNP, the project NEREIDE was funded by the EC in 2017 within the framework of LIFE projects, with the specific aim of developing innovative solutions for sustainable LNP with CRM, by using both the wet and the dry process. In the Phase I of the NEREiDE project, the mechanical and functional performances of the new LNP surfaces, developed by using the two technologies, were analysed and compared in order to assess their respective potentials for use as viable alternatives to other traditional LNP surfaces. The analysis of laboratory and on-site test results, carried out on specifically built field trials, allowed to understand strengths and weaknesses of the new LNPs in terms of mechanical and functional performance. In order to identify opportunities and threats in the analysis, the external factors that can be helpful or harmful to the recycling of CRM in LNPs have been analysed
Sustainability in railway construction: LCA-LCC based assessment of alternative solutions for track-bed
No full textThe economic and environmental sustainability of the Bitumen Stabilized Ballast (BSB) as construction and maintenance practice in railway track-bed is evaluated in comparison to the traditional ballast (TB). This aim is achieved integrating the results of an attributional Life Cycle Assessment (LCA), following a cradle-to-grave approach, and the Life Cycle Cost (LCC) analyses. The higher durability of BSB leads to arise environmental benefits in almost all the impact categories of LCA. Nevertheless, Bitumen Emulsion (BE) originates high level of impact on certain categories and they cannot be compensated by the reduction of the minor and major maintenance activities required by the BSB solution over the life cycle. The results of the LCA have been implemented in the LCC model for accounting the external costs due to the environmental impacts. From this analysis it emerges that the BSB technology, used since the construction stage and during the routine tamping, can provide economical savings
Defining machine learning algorithms as accident prediction models for Italian two-lane rural, suburban, and urban roads
No full textFour Accident Prediction Models have been defined for Italian two-lane rural, suburban, and urban roads by exploiting different Machine Learning Algorithms. Specifically, a Classification and Regression Tree, a Boosted Regression Tree, a Random Forest, and a Support Vector Machine have been implemented to predict the number of Fatal and Injury crashes on a 905-km network, which experienced 5,802 FI crashes in 2008-2016. The dataset incorporates geometrical, functional, and environmental information. Several performance metrics have been computed, such as Determination Coefficient, Mean Absolute Error, Root Mean Square Error, and scatterplots. Outcomes suggest that Support Vector Machine outperforms the other Machine Learning Algorithms for predicting Fatal and Injury crashes. In Addition, the computation of Predictor Importance shows that traffic flow, the density of intersections, driveway density, and type of area are the most impacting factors on crash likelihood. Road authorities may use these findings for conducting reliable safety analyses
Overfitting Prevention in Accident Prediction Models: Bayesian Regularization of Artificial Neural Networks
No full textIn the present paper, we implemented the Bayesian regularization (BR) backpropagation algorithm for calibrating an artificial neural network (ANN) as an accident prediction model (APM) to be used on Italian four-lane divided roads. We chose the BR-ANN since it efficiently allows for dealing with small sample size and avoiding overfitting issues by adding a regularization term in the objective function to be minimized during training. Moreover, BR-ANNs are sparsely employed in road safety analyses, and their peculiarities deserve to be emphasized. In our work, the BR-ANN aims to predict the number of fatal and injury (FI) crashes across 236 road elements, for a total length of 78 km. The input features are road element length, horizontal and vertical alignment, cross-section geometry, operating speed, traffic flow, sight distance, and road area type (i.e., a categorical predictor accounting for the potential influence of merge and diverge influence areas). Training and test phases of the BR-ANN have been evaluated by determination coefficient (R2), root mean square error (RMSE), overfitting ratio (OR), scatterplots, residuals analysis, and by the same ANN architecture trained with the gradient descent (GD) with momentum and adaptive learning rate backpropagation algorithm (GD-ANN). Results demonstrate that the BR-ANN markedly outperforms the GD-ANN, which suffers severe overfitting issues. Furthermore, BR-ANN does not overfit data (OR close to the unity), reports a satisfactory R2 (0.726), and shows a Gaussian residual distribution with zero mean. Therefore, road authorities could consider regularized ANNs for performing appropriate safety analyses, especially when dealing with small road sample sizes
Crumb Rubber Modifier in Road Asphalt Pavements: State of the Art and Statistics
Full text linkTire rubber recycling for civil engineering applications and products is developing
faster, achieving increasingly higher levels of maturation. The improvements in the material circle,
where crumb rubber, generated as a by-product of the tire rubber making process, becomes the
resource used for the construction of road asphalt pavement, is absolutely necessary for increasing
the sustainability of the entire supply chain. The paper reports the results of an accurate data
analysis derived from an extensive literature review of existing processes, technologies, and materials
within construction of infrastructure. The current position, the direction, and rate of progress of
the scientific eorts towards the reuse and recycling of tire rubber worldwide have been shown.
Furthermore, an in-depth analysis of a set of important properties of Crumb Rubber Modified
Asphalt has been carried out—fabrication parameters, standard properties, high and low-temperature
performance, and rheological properties. Statistics over a sample of selected publications have been
presented to understand the main processes adopted, rubber particle size, temperatures, and possible
further modifications of crumb rubber modified binder
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