scholarly journals Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Jungyeol Hong ◽  
Reuben Tamakloe ◽  
Dongjoo Park
Keyword(s):  
Risk Factors ◽  
Association Rules ◽  
Weather Conditions ◽  
Parametric Models ◽  
Apriori Algorithm ◽  
Drowsy Driving ◽  
Crash Data ◽  
Rainy Weather ◽  
Crash Characteristics ◽  
Strength Of Association

This study aims to discover hidden patterns and potential relationships in risk factors in freight truck crash data. Existing studies mainly used parametric models to analyze the causes of freight vehicle crashes. However, predetermined assumptions and underlying relationships between independent and dependent variables have been cited as its limitations. To overcome these limitations and provide a better understanding of factors that lead to truck crashes on the expressways, we applied the Association Rules Mining (ARM) technique, which is a nonparametric method. ARM quantifies the interrelationships between the antecedents and consequents of truck-involved crashes and provides researchers with the most influential set of factors that leads to crashes. We utilized a freight vehicle-involved crash data consisting of 19,038 crashes that occurred on the Korean expressways from 2008 to 2017 for this investigation. From the data, 90,951 association rules were generated through ARM employing the Apriori algorithm. The lift values estimated by the Apriori algorithm showed the strength of association between risk factors, and based on the estimated lift values, we identified key crash contributory factors that lead to truck-involved crashes at various segment types, under different weather conditions, considering the driver’s age, crash type, driver’s faults, vehicle size, and roadway geometry type. From the generated rules, we demonstrated that overspeeding with medium-weight trucks was highly associated with crashes during the rainy weather, whereas drowsy driving during the evening was correlated with crashes during fine weather. Segment-related crashes were mainly associated with driver’s faults and roadway geometry. Our results present useful insights and suggestions that can be used by transport stakeholders, including policymakers and researchers, to create relevant policies that will help reduce freight truck crashes on the expressways.

scholarly journals Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

2019 ◽  
Vol 19 (3) ◽  
pp. 154-167 ◽  
Author(s):  
Indah Werdiningsih ◽  
Rimuljo Hendradi ◽  
Barry Nuqoba ◽  
Elly Ana ◽  
Keyword(s):  
Risk Factors ◽  
Early Childhood ◽  
Association Rules ◽  
Principal Component ◽  
Feature Reduction ◽  
Apriori Algorithm ◽  
Childhood Diseases ◽  
Frequent Item ◽  
Computing Performance ◽  
Frequent Item Sets

Abstract This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was developed based on Principal Component Analysis (PCA) method. Previous research using Apriori algorithm for association rule mining only managed to get the frequent item sets, so it could only find the frequent association rules. Other studies used ARIMA algorithm and succeeded in obtaining the rare item sets and the rare association rules. The approach proposed in this study was to obtain all the complete sets including the frequent item sets and rare item sets with feature reduction. A series of experiments with several parameter values were extrapolated to analyze and compare the computing performance and rules produced by Apriori algorithm, ARIMA, and the proposed approach. The experimental results show that the proposed approach could yield more complete rules and better computing performance.

scholarly journals Risk Factors Affecting Traffic Accidents at Urban Weaving Sections: Evidence from China

2019 ◽  
Vol 16 (9) ◽  
pp. 1542 ◽  
Author(s):  
Xinhua Mao ◽  
Changwei Yuan ◽  
Jiahua Gan ◽  
Shiqing Zhang
Keyword(s):  
Risk Factors ◽  
Traffic Accidents ◽  
Multinomial Logistic Regression ◽  
Weather Condition ◽  
Weather Conditions ◽  
Traffic Density ◽  
Vehicle Speed ◽  
Factors Affecting ◽  
Traffic Characteristics ◽  
Rainy Weather

As a critical configuration of interchanges, the weaving section is inclined to be involved in more traffic accidents, which may bring about severe casualties. To identify the factors associated with traffic accidents at the weaving section, we employed the multinomial logistic regression approach to identify the correlation between six categories of risk factors (drivers’ attributes, weather conditions, traffic characteristics, driving behavior, vehicle types and temporal-spatial distribution) and four types of traffic accidents (rear-end, side wipe, collision with fixtures and rollover) based on 768 accident samples of an observed weaving section from 2016 to 2018. The modeling results show that drivers’ gender and age, weather condition, traffic density, weaving ratio, vehicle speed, lane change behavior, private cars, season, time period, day of week and accident location are important factors affecting traffic accidents at the weaving section, but they have different contributions to the four traffic accident types. The results also show that traffic density of ≥31 vehicle/100 m has the highest risk of causing rear-end accidents, weaving ration of ≥41% has the highest possibility to bring about a side wipe incident, collision with fixtures is the most likely to happen in snowy weather, and rollover is the most likely incident to occur in rainy weather.

scholarly journals Investigating the Risk Factors Associated with the Severity of the Pedestrians Injured on Spanish Crosstown Roads

2019 ◽  
Vol 11 (19) ◽  
pp. 5194 ◽  
Author(s):  
Natalia Casado-Sanz ◽  
Begoña Guirao ◽  
Antonio Lara Galera ◽  
Maria Attard
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Weather Conditions ◽  
Crash Data ◽  
Factors Associated ◽  
The Road ◽  
Pedestrian Injury ◽  
On The Road ◽  
Pedestrian Crashes

According to the Spanish General Traffic Accident Directorate, in 2017 a total of 351 pedestrians were killed, and 14,322 pedestrians were injured in motor vehicle crashes in Spain. However, very few studies have been conducted in order to analyse the main factors that contribute to pedestrian injury severity. This study analyses the accidents that involve a single vehicle and a single pedestrian on Spanish crosstown roads from 2006 to 2016 (1535 crashes). The factors that explain these accidents include infractions committed by the pedestrian and the driver, crash profiles, and infrastructure characteristics. As a preliminary tool for the segmentation of 1535 pedestrian crashes, a k-means cluster analysis was applied. In addition, multinomial logit (MNL) models were used for analysing crash data, where possible outcomes were fatalities and severe and minor injured pedestrians. According to the results of these models, the risk factors associated with pedestrian injury severity are as follows: visibility restricted by weather conditions or glare, infractions committed by the pedestrian (such as not using crossings, crossing unlawfully, or walking on the road), infractions committed by the driver (such as distracted driving and not respecting a light or a crossing), and finally, speed infractions committed by drivers (such as inadequate speed). This study proposes the specific safety countermeasures that in turn will improve overall road safety in this particular type of road.

scholarly journals Mining association rules between stroke risk factors based on the Apriori algorithm

2017 ◽  
Vol 25 ◽  
pp. 197-205 ◽  
Author(s):  
Qin Li ◽  
Yiyan Zhang ◽  
Hongyu Kang ◽  
Yi Xin ◽  
Caicheng Shi
Keyword(s):  
Risk Factors ◽  
Association Rules ◽  
Stroke Risk ◽  
Apriori Algorithm ◽  
Stroke Risk Factors ◽  
Mining Association Rules

Potential Benefits of Animal-Detecting Automatic Emergency Braking Systems Based on U.S. Driving Data

2021 ◽  
pp. 036119812110124
Author(s):  
Jeremy A. Decker ◽  
Samantha H. Haus ◽  
Rini Sherony ◽  
Hampton C. Gabler
Keyword(s):  
Weather Conditions ◽  
Target Population ◽  
Low Light ◽  
Emergency Braking ◽  
Target Populations ◽  
Light Trucks ◽  
Naturalistic Driving Study ◽  
Potential Benefits ◽  
Nationally Representative ◽  
Crash Characteristics

In 2015, there were 319,195 police reported vehicle-animal crashes, resulting in 275 vehicle occupant fatalities. Animal-detecting automatic emergency braking (AEB) systems are a promising active safety measure which could potentially avoid or mitigate many of these crashes by warning the driver, utilizing automatic braking, or both. The purpose of this study was to develop and characterize a target population of vehicle-animal crashes applicable to AEB systems and to analyze the potential benefits of an animal-detecting AEB system. The study was based on two nationally representative databases, Fatality Analysis Reporting System and the National Automotive Sampling System’s General Estimates System, and a naturalistic driving study, SHRP 2. The target population was restricted to vehicle-animal crashes that were forward impacts or road departures and involved cars and light trucks, with no loss of control. Crash characteristics which may influence the performance of AEB such as lighting, weather, pre-crash movement, relation to junction, and first and worst harmful events, were analyzed. The study found that the major influences on the effectiveness of animal AEB systems were: weather, lighting, pre-crash movements, and the crash location. Six potential target populations were used to analyze the potential effectiveness of an animal AEB system, with effectiveness ranging between 21.6% and 97% of police reported crashes and between 4.1% and 50.8% of fatal vehicle-animal crashes. An AEB system’s ability to function in low light and poor weather conditions may enable it to avoid a substantially higher proportion of crashes.

scholarly journals Winter Wheat Straw Decomposition under Different Nitrogen Fertilizers

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 83
Author(s):  
Gabriela Mühlbachová ◽  
Pavel Růžek ◽  
Helena Kusá ◽  
Radek Vavera ◽  
Martin Káš
Keyword(s):  
Winter Wheat ◽  
Wheat Straw ◽  
Soil Surface ◽  
Weather Conditions ◽  
Mineral Nitrogen ◽  
Nitrogen Fertilizers ◽  
Pig Slurry ◽  
Straw Decomposition ◽  
Rainy Weather ◽  
N Fertilisers

The climate changes and increased drought frequency still more frequent in recent periods bring challenges to management with wheat straw remaining in the field after harvest and to its decomposition. The field experiment carried out in 2017–2019 in the Czech Republic aimed to evaluate winter wheat straw decomposition under different organic and mineral nitrogen fertilizing (urea, pig slurry and digestate with and without inhibitors of nitrification (IN)). Treatment Straw 1 with fertilizers was incorporated in soil each year the first day of experiment. The Straw 2 was placed on soil surface at the same day as Straw 1 and incorporated together with fertilizers after 3 weeks. The Straw 1 decomposition in N treatments varied between 25.8–40.1% and in controls between 21.5–33.1% in 2017–2019. The Straw 2 decomposition varied between 26.3–51.3% in N treatments and in controls between 22.4–40.6%. Higher straw decomposition in 2019 was related to more rainy weather. The drought observed mainly in 2018 led to the decrease of straw decomposition and to the highest contents of residual mineral nitrogen in soils. The limited efficiency of N fertilisers on straw decomposition under drought showed a necessity of revision of current strategy of N treatments and reduction of N doses adequately according the actual weather conditions.

scholarly journals Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 32
Author(s):  
Syed As-Sadeq Tahfim ◽  
Chen Yan
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
The United States ◽  
Clustering Methods ◽  
Severe Injuries ◽  
Traffic Crash ◽  
Crash Data ◽  
Boosted Decision Trees ◽  
The Individual ◽  
Crash Characteristics

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

Improved Apriori Algorithm in the Power System Fault

2014 ◽  
Vol 556-562 ◽  
pp. 1510-1514
Author(s):  
Li Qiang Lin ◽  
Hong Wen Yan
Keyword(s):  
Power System ◽  
Association Rules ◽  
Fault Analysis ◽  
The Other ◽  
Apriori Algorithm ◽  
Extreme Climate ◽  
Time Space ◽  
Low Efficiency ◽  
Candidate Item ◽  
Improved Algorithm

For the low efficiency in generating candidate item sets of apriori algorithm, this paper presents a method based on property division to improve generating candidate item sets. Comparing the improved apriori algorithm with the other algorithm and the improved algorithm is applied to the power system accident cases in extreme climate. The experiment results show that the improved algorithm significantly improves the time efficiency of generating candidate item sets. And it can find the association rules among time, space, disasters and fault facilities in the power system accident cases in extreme climate. That is very useful in power system fault analysis.

Exploring Insights through Visualization of Association Rules from Text Mining Statistics

2013 ◽  
Vol 310 ◽  
pp. 567-571
Author(s):  
Arun Thotapalli Sundararaman
Keyword(s):  
Text Mining ◽  
Association Rules ◽  
Decision Makers ◽  
Analysis Of Knowledge ◽  
Strength Of Association ◽  
The Way

Visualization is an important technique for analysis of knowledge derived from text mining. While different approaches exist for visualization, this paper presents a novel way of visualizing the strength of association between multiple terms that summarizes association in the form of a matrix. This approach is expected to improve the way decision makers analyze insights from text mining.

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