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 Comments on "Researcher bias: The use of machine learning in software defect prediction"

2015 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Prediction Model ◽  
Strong Association ◽  
Model Performance ◽  
Strong Relationship ◽  
Defect Prediction ◽  
Explanatory Variables ◽  
Software Defect ◽  
The Impact ◽  
The Relationship ◽  
Selection Of

Shepperd et al. (2014) find that the reported performance of a defect prediction model shares a strong relationship with the group of researchers who construct the models. In this paper, we perform an alternative investigation of Shepperd et al. (2014)’s data. We observe that (a) researcher group shares a strong association with the dataset and metric families that are used to build a model; (b) the strong association among the explanatory variables introduces a large amount of interference when interpreting the impact of the researcher group on model performance; and (c) after mitigating the interference, we find that the researcher group has a smaller impact than the metric family. These observations lead us to conclude that the relationship between the researcher group and the performance of a defect prediction model may have more to do with the tendency of researchers to reuse experimental components (e.g., datasets and metrics). We recommend that researchers experiment with a broader selection of datasets and metrics to combat potential bias in their results.

scholarly journals Nothing good happens after dark: the influence of temporal factors on motor carrier crash severity

2018 ◽  
Vol 28 (2) ◽  
pp. 7-18
Author(s):  
Misty Moody ◽  
S Scott Nadler ◽  
Doug Voss
Keyword(s):  
Public Policy ◽  
Time Of Day ◽  
Regulatory Agencies ◽  
Future Research ◽  
Crash Severity ◽  
Property Damage ◽  
Motor Carrier ◽  
Temporal Factors ◽  
Safety Outcomes ◽  
Day Of The Week

Motor carrier safety is a topic of great importance for both industry and makers of public policy. Regulatory agencies, such as the Federal Motor Carrier Safety Administration (FMCSA), regularly publish data detailing the circumstances surrounding roadway accidents. FMCSA’s Large Truck and Bus Crash Facts (LTBCF) data demonstrate an increase in accidents during daylight hours and on weekdays. Roadway risks are ever-present but differ by time of day and day of the week. These differences may potentially engender crashes of different severities at different times. This study analyzes FMCSA LTBCF data to determine when crashes of different severities are more likely to occur. Findings indicate that crashes resulting in property damage are more likely to occur during the day and on weekdays. However, fatal and injury crashes are significantly more likely during nights and weekends. Recommendations to improve safety outcomes are provided along with suggestions for future research.

Research Status and Development Trends of Hazardous Materials Transportation

2011 ◽  
Vol 361-363 ◽  
pp. 1230-1239
Author(s):  
Zong Feng Zou ◽  
Bao Quan Zhang
Keyword(s):  
Hazardous Materials ◽  
Future Research ◽  
Joint Control ◽  
Large Area ◽  
Role Of Government ◽  
Hazardous Materials Transportation ◽  
Transportation Risk ◽  
Hazmat Transportation ◽  
Research Situation

The related issues of hazardous materials transportation in recent years are summarized and reviewed from the following aspects: hazardous materials transportation risk evaluation models, road routing models, the application of related technology, early warning for emergency response and joint action mechanism and platform construction, the research situation and development pattern of unified monitoring platform, etc. Analysis shows that it is essential to establish more in-depth and scientific quantitative models based on the attainment of more comprehensive and continuous data as well as the consideration of various constraints. It is a direction for future research to develop comprehensive application of technology and to establish HAZMAT transportation joint control platform in large area, and the leading and facilitating role of government should be paid more attention on joint control platform construction in large area.

scholarly journals Routing for Hazardous Materials Transportation in Urban Areas

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1091 ◽  
Author(s):  
Zhang ◽  
Feng ◽  
Yang ◽  
Ding
Keyword(s):  
Urban Areas ◽  
Optimal Solution ◽  
Hazardous Materials ◽  
Work Load ◽  
Decision Variable ◽  
Daily Production ◽  
Hazardous Materials Transportation ◽  
Decision Variables ◽  
Hazmat Transportation ◽  
Local Risk

Hazardous materials (HAZMAT) are important for daily production in cities, which usually have a high population. To avoid the threat to public safety and security, the routes for HAZMAT transportation should be planned legitimately by mitigating the maximum risk to population centers. For the objective of min-max local risk in urban areas, this study has newly proposed an optimization model where the service of a link for HAZMAT transportation was taken as the key decision variable. Correspondingly, the symmetric problem of min-max optimization takes significant meanings. Moreover, in consideration of the work load of solving the model under a lot of decision variables, a heuristic algorithm was developed to obtain an optimal solution. Thereafter, a case study was made to test the proposed model and algorithm, and the results were compared with those generated by deterministic solving approaches. In addition, this research is able to be an effective reference for authorities on the management of HAZMAT transportation in urban areas.

scholarly journals Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning

2020 ◽  
Vol 10 (21) ◽  
pp. 7741
Author(s):  
Sang Yeob Kim ◽  
Gyeong Hee Nam ◽  
Byeong Mun Heo
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Strong Association ◽  
Forced Expiratory Volume ◽  
Binary Logistic Regression Analysis ◽  
Cross Sectional Survey ◽  
Cross Sectional

Metabolic syndrome (MS) is an aggregation of coexisting conditions that can indicate an individual’s high risk of major diseases, including cardiovascular disease, stroke, cancer, and type 2 diabetes. We conducted a cross-sectional survey to evaluate potential risk factor indicators by identifying relationships between MS and anthropometric and spirometric factors along with blood parameters among Korean adults. A total of 13,978 subjects were enrolled from the Korea National Health and Nutrition Examination Survey. Statistical analysis was performed using a complex sampling design to represent the entire Korean population. We conducted binary logistic regression analysis to evaluate and compare potential associations of all included factors. We constructed prediction models based on Naïve Bayes and logistic regression algorithms. The performance evaluation of the prediction model improved the accuracy with area under the curve (AUC) and calibration curve. Among all factors, triglyceride exhibited a strong association with MS in both men (odds ratio (OR) = 2.711, 95% confidence interval (CI) [2.328–3.158]) and women (OR = 3.515 [3.042–4.062]). Regarding anthropometric factors, the waist-to-height ratio demonstrated a strong association in men (OR = 1.511 [1.311–1.742]), whereas waist circumference was the strongest indicator in women (OR = 2.847 [2.447–3.313]). Forced expiratory volume in 6s and forced expiratory flow 25–75% strongly associated with MS in both men (OR = 0.822 [0.749–0.903]) and women (OR = 1.150 [1.060–1.246]). Wrapper-based logistic regression prediction model showed the highest predictive power in both men and women (AUC = 0.868 and 0.932, respectively). Our findings revealed that several factors were associated with MS and suggested the potential of employing machine learning models to support the diagnosis of MS.

Multiobjective Routing and Scheduling of Hazardous Materials Shipments

1998 ◽  
Vol 1613 (1) ◽  
pp. 96-104 ◽  
Author(s):  
G. Sulijoadikusumo ◽  
L. Nozick
Keyword(s):  
Hazardous Materials ◽  
Transportation System ◽  
Time Of Day ◽  
Multiple Objectives ◽  
Hazardous Material ◽  
Routing And Scheduling ◽  
Scheduling Analysis ◽  
Explicit Consideration ◽  
And Performance

Good routing and scheduling decisions for hazardous materials shipments often require the explicit consideration of multiple objectives. Also, the performance of the relevant facilities in the transportation system typically varies by time of day with respect to many of these objectives. The authors discuss the reliability and performance of a heuristic that can be used to identify good routes and schedules for hazardous material shipments. Presented is the second part of a two-part analysis. The first part described a method for performing an integrated routing/ scheduling analysis with multiple objectives when the arc attributes are time variant. This part discusses the quality of that analysis and additional heuristics that can be used to improve the quality of the solution generated.

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 Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 54-59
Author(s):  
Marco Febriadi Kokasih ◽  
Adi Suryaputra Paramita
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Fair Price ◽  
Price Prediction ◽  
Online Marketplace ◽  
Extreme Gradient Boosting ◽  
Property Owners ◽  
Boosting Algorithm

Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.

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