A Data Mining Approach to Identify Key Factors of Traffic Injury Severity

Kashani, Ali Tavakoli; Mohaymany, Afshin Shariat; Ranjbari, Andishe

doi:10.7307/ptt.v23i1.144

Cited by 37 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These criteria were based on an analysis of the distributions of the samples of the four original driver-injury classes, among which there is an important imbalance of data (there are few dead drivers compared to the number of minor or unharmed drivers). As other authors point out [27,31,39], it is more efficient to work with balanced data, which is the approach that has been adopted in this work.…”

Section: Data Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

et al. 2020

View full text Add to dashboard Cite

The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.

show abstract

Section: Data Descriptionmentioning

confidence: 99%

“…To reduce type-1 errors considering cross validation, the dataset was split randomly into two parts: a training set (70% of the data) and a testing set (the remaining 30%), as done in previous works [31,39].…”

Section: Classification Tree Model (Cart)mentioning

confidence: 99%

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Depaire et al [13] used clustering technique to analyze road accident data of Belgium and suggest that cluster-based analysis of road accident data can extract better information rather analyzing data without clustering. Kashani et al [14] used classification and regression tree (CART) to analyze road accidents data of Iran and found that not using seat belt, improper overtaking and speeding badly affect the severity of accidents. Kwon et al [15] used Naïve Bayes and decision tree classification algorithm to analyze factor dependencies related to road safety.…”

Section: Introductionmentioning

confidence: 99%

A data mining approach to characterize road accident locations

2016

View full text Add to dashboard Cite

Data mining has been proven as a reliable technique to analyze road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. However, any damage resulting from road accidents is always unacceptable in terms of health, property damage and other economic factors. Sometimes, it is found that road accident occurrences are more frequent at certain specific locations. The analysis of these locations can help in identifying certain road accident features that make a road accident to occur frequently in these locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this paper, we first applied k-means algorithm to group the accident locations into three categories, high-frequency, moderate-frequency and low-frequency accident locations. k-means algorithm takes accident frequency count as a parameter to cluster the locations. Then we used association rule mining to characterize these locations. The rules revealed different factors associated with road accidents at different locations with varying accident frequencies. The association rules for high-frequency accident location disclosed that intersections on highways are more dangerous for every type of accidents. High-frequency accident locations mostly involved two-wheeler accidents at hilly regions. In moderate-frequency accident locations, colonies near local roads and intersection on highway roads are found dangerous for pedestrian hit accidents. Low-frequency accident locations are scattered throughout the district and the most of the accidents at these locations were not critical. Although the data set was limited to some selected attributes, our approach extracted some useful hidden information from the data which can be utilized to take some preventive efforts in these locations.

show abstract

“…The disadvantage of the parameter model-based method, such as regression analysis, is that the predefined underlying relationships between different variables could produce an erroneous estimation of the accident likelihood. The classification and regression tree (CART) is a powerful tool used for factor classification and ranking by importance [13]. It has been widely used in the crash mechanism and accident frequency analysis, traffic flow estimation and injury severity estimation [14][15][16].…”

Section: Dynamic Bayesian Network-based Escape Probability Estimation For Coach Fire Accidentsmentioning

confidence: 99%

Dynamic Bayesian Network-Based Escape Probability Estimation for Coach Fire Accidents

Zhou

Zhao

et al. 2021

PROMET

View full text Add to dashboard Cite

Coach emergency escape research is an effective measure to reduce casualties under serious vehicle fire accidents. A novel experiment method employing a wireless transducer was implemented and the head rotation speed, rotation moment and rotation duration were collected as the input variables for the classification and regression tree (CART) model. Based on this model, the classification result explicitly pointed out that the exit searching efficiency was evolving. By ignoring the last three unimportant factors from the Analytic Hierarchy Process (AHP), the ultimate Dynamic Bayesian Network (DBN) was built with the temporal part of the CART output and the time-independent part of the vehicle characteristics. Simulation showed that the most efficient exit searching period is the middle escape stage, which is 10 seconds after the emergency signal is triggered, and the escape probability clearly increases with the efficient exit searching. Furthermore, receiving emergency escape training contributes to a significant escape probability improvement of more than 10%. Compared with different failure modes, the emergency hammer layout and door reliability have a more significant influence on the escape probability improvement than aisle condition. Based on the simulation results, the escape probability will significantly drop below 0.55 if the emergency hammers, door, and aisle are all in a failure state.

show abstract

A Data Mining Approach to Identify Key Factors of Traffic Injury Severity

Cited by 37 publications

References 17 publications

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

A data mining approach to characterize road accident locations

Dynamic Bayesian Network-Based Escape Probability Estimation for Coach Fire Accidents

Contact Info

Product

Resources

About