Extracting decision rules from police accident reports through decision trees

Oña, Juan de; López, Griselda; Abellán, Joaquín

doi:10.1016/j.aap.2012.09.006

Cited by 108 publications

(75 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DTs, and more specifically the Classification and Regression Tree (CART) methods, are frequently applied in the field of safety analysis (Kuhnert et al, 2000;Karlaftis and Golias, 2002;Park and Saccomanno, 2005;Chang and Wang, 2006;Harb et al, 2009;Kashani and Mohaymany, 2011;De Oña et al, 2012;Montella et al, 2012;Abellán et al, 2013;De Oña et al, 2013). A main advantage of DTs is that their structure permits the extraction of Decision Rules (DRs) of the type "if-then".…”

Section: Introductionmentioning

confidence: 99%

Using Decision Trees for Comparing Different Consistency Models

Garach

Poyo

Maldonado

2014

Procedia - Social and Behavioral Sciences

View full text Add to dashboard Cite

One technique used to improve highway safety from the point of view of the infrastructure is to examine the consistency of the design. Design consistency refers to if highway geometry is conformance to driver expectancy. When the consistency of the road is inadequate, the more likely it is that drivers will be startled and a crash will occur. The consistency, based on operating speed, has been calculated in Spanish two-lane rural highways. This consistency has been evaluated using a local method, to measure the consistency of each element of the road and using a global method, to measure the consistency of a segment of the road. Different models of consistency have been compared using Decision Trees (DTs). DTs are a Data Mining Techniques which can be used to solve classification problems. The results show that DTs are a suitable technique to compare consistence models and they permit to establish limits between the different models analyzed.

show abstract

Section: Introductionmentioning

confidence: 99%

Using Decision Trees for Comparing Different Consistency Models

Garach

Poyo

Maldonado

2014

Procedia - Social and Behavioral Sciences

View full text Add to dashboard Cite

show abstract

“…Pande and Abdel-Aty [9] concluded that there is a significant correlation between lack of illumination and high crash severity. de Oña et al [10,11] pointed out that fatal accidents are associated with roadways with no lighting. Wanvik [12] found that good road lighting can decrease road accidents by one-half based upon a study of 763,000 injury accidents and 3.3 million property-damage accidents in Norway.…”

Section: Effects Of Light Condition On Crashesmentioning

confidence: 99%

Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

Wei

Shu

Huang

et al. 2017

Journal of Advanced Transportation

View full text Add to dashboard Cite

Previous studies have investigated various factors that contribute to the severity of work zone crashes. However, little has been done on the specific effects of light conditions. Using the data from the Enhanced Tennessee Roadway Information Management System (E-TRIMS), crashes that occurred in the Tennessee work zones during 2003-2015 are categorized into three light conditions: daylight, dark-lighted, and dark-not-lighted. One commonly used decision tree method-Classification and Regression Trees (CART)-is adopted to investigate the factors contributing to crash severity in highway work zones under these light conditions. The outcomes from the three decision trees with differing light conditions show significant differences in the ranking and importance of the factors considered in the study, thereby indicating the necessity of examining traffic crashes according to light conditions. By separately considering the crash characteristics under different light conditions, some new findings are obtained from this study. The study shows that an increase in the number of lanes increases the crash severity level in work zones during the day while decreasing the severity at night. Similarly, drugs and alcohol are found to increase the severity level significantly under the dark-not-lighted condition, while they have a limited influence under daylight and dark-lighted conditions.

show abstract

“…Juan de ona et al [7] discusses road accidents, analysts many road safety is to identify the main factors that give to crash severity. They applied decision tree approach in the road safety field.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Classification Through Machine Learning Technique: C4. 5 Algorithm based on Various Entropies

Sharma¹,

Agrawal²,

Sharma³

2013

IJCA

View full text Add to dashboard Cite

Data mining is an interdisciplinary field of computer science and is referred to extracting or mining knowledge from large amounts of data. Classification is one of the data mining techniques that maps the data into the predefined classes and groups. It is used to predict group membership for data instances. There are many areas that adapt Data mining techniques such as medical, marketing, telecommunications, and stock, health care and so on. The C4.5 can be referred as the statistic Classifier. This algorithm uses gain radio for feature selection and to construct the decision tree. It handles both continuous and discrete features. C4.5 algorithm is widely used because of its quick classification and high precision. This paper proposed a C4.5 classifier based on the various entropies (Shannon Entropy, Havrda and Charvt entropy, Quadratic entropy) instance of Shannon entropy for classification. Experiment results show that the various entropy based approach is effective in achieving a high classification rate.

show abstract

Extracting decision rules from police accident reports through decision trees

Cited by 108 publications

References 26 publications

Using Decision Trees for Comparing Different Consistency Models

Using Decision Trees for Comparing Different Consistency Models

Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

Classification Through Machine Learning Technique: C4. 5 Algorithm based on Various Entropies

Contact Info

Product

Resources

About