Severity analysis of road transport accidents of hazardous materials with machine learning

2021 ◽  
pp. 1-6
Author(s):  
Xiaoyan Shen ◽  
Shanshan Wei
Keyword(s):  
Machine Learning ◽  
Hazardous Materials ◽  
Road Transport ◽  
Transport Accidents

scholarly journals Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads

2021 ◽  
Vol 13 (22) ◽  
pp. 12773
Author(s):  
Shanshan Wei ◽  
Xiaoyan Shen ◽  
Minhua Shao ◽  
Lijun Sun
Keyword(s):  
Prediction Model ◽  
Strong Association ◽  
Hazardous Materials ◽  
Road Transport ◽  
Time Of Day ◽  
Property Damage ◽  
Rural Roads ◽  
Extreme Gradient Boosting ◽  
Hazmat Transportation ◽  
Transport Accidents

With the increase in the demand for and transportation of hazardous materials (Hazmat), frequent Hazmat road transport accidents, high death tolls and property damage have caused widespread societal concern. Therefore, it is necessary to carry out risk factor analysis of Hazmat transportation; predict the severity of accidents; and develop targeted, extensive and refined preventive measures to guarantee the safety of Hazmat road transportation. Based on the philosophy of graded risk management, this study used a priori algorithms in association rule mining (ARM) technology to analyze Hazmat transport accidents, using road types as classification criteria to find rules that had strong associations with property-damage-only (PDO) accidents and casualty (CAS) accidents under different road types. The results indicated that accidents involving PDO had a strong association with weather (WEA), traffic signals (TS), surface conditions (SC), fatigue (FAT) and vehicle safety status (VSS), and that accidents involving CAS had a strong association with VSS, equipment safety status (ESS), time of day (TOD) and WEA when urban roads were used for Hazmat transportation. Among Hazmat transport incidents on rural roads, the incidence of PDO accidents was associated with intersections (IN), SC, WEA, vehicle type (VT), and segment type (ST), while the occurrence of CAS accidents was associated with qualification (QUA), ESS, TS, VSS, SC, WEA, TOD, and month (MON). Strong associations between the occurrence of PDO accidents and related items, such as IN, SC, WEA and FAT, and the occurrence of CAS accidents and related items, such as ESS, TOD, VSS, WEA and SC, were identified for Hazmat road transport accidents on highways. The accident characteristics exemplified by strongly correlated rules were used as the input to the prediction model. Considering the scarcity of these events, four prediction models were selected to predict the severity of Hazmat accidents on each road type employing four analyses, and the most suitable prediction model was determined based on the evaluation criteria. The results showed that extreme gradient boosting (XGBoost) is preferable for predicting the severity of Hazmat accidents occurring on urban roads and highways, while nearest neighbor classification (NNC) is more suitable for predicting the severity of Hazmat accidents occurring on rural roads.

scholarly journals Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 953
Author(s):  
Nipun Gunawardena ◽  
Giuliana Pallotta ◽  
Matthew Simpson ◽  
Donald D. Lucas
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Initial Conditions ◽  
Atmospheric Transport ◽  
Hazardous Materials ◽  
Atmospheric Model ◽  
Weather Data ◽  
Uncertainty Estimates ◽  
Large Ensembles ◽  
Physics Ensemble

In the event of an accidental or intentional hazardous material release in the atmosphere, researchers often run physics-based atmospheric transport and dispersion models to predict the extent and variation of the contaminant spread. These predictions are imperfect due to propagated uncertainty from atmospheric model physics (or parameterizations) and weather data initial conditions. Ensembles of simulations can be used to estimate uncertainty, but running large ensembles is often very time consuming and resource intensive, even using large supercomputers. In this paper, we present a machine-learning-based method which can be used to quickly emulate spatial deposition patterns from a multi-physics ensemble of dispersion simulations. We use a hybrid linear and logistic regression method that can predict deposition in more than 100,000 grid cells with as few as fifty training examples. Logistic regression provides probabilistic predictions of the presence or absence of hazardous materials, while linear regression predicts the quantity of hazardous materials. The coefficients of the linear regressions also open avenues of exploration regarding interpretability—the presented model can be used to find which physics schemes are most important over different spatial areas. A single regression prediction is on the order of 10,000 times faster than running a weather and dispersion simulation. However, considering the number of weather and dispersion simulations needed to train the regressions, the speed-up achieved when considering the whole ensemble is about 24 times. Ultimately, this work will allow atmospheric researchers to produce potential contamination scenarios with uncertainty estimates faster than previously possible, aiding public servants and first responders.

scholarly journals Protective Behaviour of Citizens to Transport Accidents Involving Hazardous Materials: A Discrete Choice Experiment Applied to Populated Areas nearby Waterways

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0142507 ◽  
Author(s):  
Esther W. de Bekker-Grob ◽  
Arnold D. Bergstra ◽  
Michiel C. J. Bliemer ◽  
Inge J. M. Trijssenaar-Buhre ◽  
Alex Burdorf
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Hazardous Materials ◽  
Protective Behaviour ◽  
Transport Accidents

scholarly journals Machine Learning in Hazardous Building Material Management: Research Status and Applications

2021 ◽  
Vol 03 (02) ◽  
pp. 1-1
Author(s):  
Pei-Yu Wu ◽  
◽  
Kristina Mjörnell ◽  
Claes Sandels ◽  
Mikael Mangold ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Hazardous Materials ◽  
Machine Learning Algorithms ◽  
Construction And Demolition Waste ◽  
The European Union ◽  
Material Management ◽  
Demolition Waste ◽  
Research Status ◽  
Management Protocol

Assessment of the presence of hazardous materials in buildings is essential for improving material recyclability, increasing working safety, and lowering the risk of unforeseen cost and delay in demolition. In light of these aspects, machine learning has been viewed as a promising approach to complement environmental investigations and quantify the risk of finding hazardous materials in buildings. In view of the increasing number of related studies, this article aims to review the research status of hazardous material management and identify the potential applications of machine learning. Our exploratory study consists of a two-fold approach: science mapping and critical literature review. By evaluating the references acquired from a literature search and complementary materials, we have been able to pinpoint and discuss the research gaps and opportunities. While pilot research has been conducted in the identification of hazardous materials, source separation and collection, extensive adoption of the available machine learning methods was not found in this field. Our findings show that (1) quantification of asbestos-cement roofing is possible from the combination of remote sensing and machine learning algorithms, (2) characterization of buildings with asbestos-containing materials is progressive by using statistical methods, and (3) separation and collection of asbestos-containing wastes can be addressed with a hybrid of image processing and machine learning algorithms. Analysis from this study demonstrates the method applicability and provides an orientation to the future implementation of the European Union Construction and Demolition Waste Management Protocol. Furthermore, establishing a comprehensive environmental inventory database is a key to facilitating a transition toward hazard-free circular construction.

Children death rate as a result of external causes

1999 ◽  
Vol 80 (6) ◽  
pp. 456-458
Author(s):  
F. R. Umyarova
Keyword(s):  
Death Rate ◽  
Road Transport ◽  
Rural Regions ◽  
Unnatural Death ◽  
The Social ◽  
External Causes ◽  
Transport Accidents

The social and hygienic aspects of the reath rate of children and teenagers as a result of accidents, traumas and intoxication are studied. Higher level of the death rate of children living in rural regions, especially of males is revealed. The basic cause of unfavourable incomes in unnatural death in children aged up to one was asphyxia as a result of aspiration of food and vomiting, older than one were drowning and road-transport accidents, from 10 to 14 suicides, from 15 to 17 suicides and road-transport accidents. The coordinated and interdisciplinary measures are necessary to solve the problem connected with accidents and injuries in children.

scholarly journals Machine learning model development for predicting road transport GHG emissions in Canada

2019 ◽  
Vol 53 (2) ◽  
pp. 55-72
Author(s):  
Mohd Jawad Ur Rehman Khan ◽  
Anjali Awasthi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Ghg Emissions ◽  
Predictive Performance ◽  
Road Transport ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Artificial Neural

Abstract Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.

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