Data mining on road safety: factor assessment on vehicle accidents using classification models

Castro, Yuri; Kim, Young Jin

doi:10.1080/13588265.2015.1122278

Cited by 59 publications

(19 citation statements)

References 7 publications

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“…In this study, the ANN model compared with the proposed Hybrid model. The Comparative performance of both models showed that the proposed model (Hybrid K means and random forest) performed better than the ANN model in terms of Precision, Recall, F1 score, Gu et al [21] PSO-SVM China -Xiao et al [52] SVM, KNN (Ensemble) I-880 data set 99.33% Castro et al [15] BN, JR8 and MLP DVSA-UK 72.39%, 72.02%, 71.70% Respectively Al-Radaideh et al [4] RF, ANN (backpropagation), SVM Uk 80.6%, 61.4%, 54.8% respectively Casado et al [14] LCC, MNL Spain -Wahab et al [51] MLP. SimpleCart, PART Ghana 72.16%, 73.45%, 73.81% respectively Sameen et al [40] MLP, BLR, RNN Malaysia 65.48%, 58.30%, 71.77% respectively Fentahun [18] J48, ID3, PART Ethiopia 81.21%, 81.01%, 81.18% Seid et al [42] HMR Ethiopia NA Abebe et al [1] DSA Ethiopia -Lytin et al [30] UBA Ethiopia - and Accuracy.…”

Section: Comparative Of Neural Network and Proposed Modelsmentioning

confidence: 98%

Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach

Yassin

Pooja

2020

SN Appl. Sci.

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Road accident severity is a major concern of the world, particularly in underdeveloped countries. Understanding the primary and contributing factors may combat road traffic accident severity. This study identified insights and the most significant target specific contributing factors for road accident severity. To get the most determinant road accident variables, a hybrid K-means and random forest (RF) approaches developed. K-means extract hidden information from road accident data and creates a new feature in the training set. The distance between each cluster and the joining line of k1 and k9 calculated and selected maximum value as k. k is an optimal value for the partition of the training set. RF employed to classify severity prediction. After comparing with other classification techniques, the result revealed that among classification techniques, the proposed approach disclosed an accuracy of 99.86%. The target-specific model interpretation result showed that driver experience and day, light condition, driver age, and service year of the vehicle were the strong contributing factors for serious injury, light injury, and fatal severity, respectively. The outcome demonstrates the predictive supremacy of the approach in road accident prediction. Road transport and insurance agencies will be benefited from the study to develop road safety strategies.

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Section: Comparative Of Neural Network and Proposed Modelsmentioning

confidence: 98%

Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach

Yassin

Pooja

2020

SN Appl. Sci.

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“…This includes evaluating the severity of traffic collisions, analysing their causes and/or predicting the probability of fatal and serious injuries. Previous research efforts demonstrated that Bayesian networks predict collision severity better than traditional methods such as regression models [24,25].…”

Section: Approachmentioning

confidence: 99%

Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment

Aldred

Herrero

Anaya

et al. 2019

IJERPH

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This study analyses factors associated with cyclist injury severity, focusing on vehicle type, route environment, and interactions between them. Data analysed was collected by Spanish police during 2016 and includes records relating to 12,318 drivers and cyclist involving in collisions with at least one injured cyclist, of whom 7230 were injured cyclists. Bayesian methods were used to model relationships between cyclist injury severity and circumstances related to the crash, with the outcome variable being whether a cyclist was killed or seriously injured (KSI) rather than slightly injured. Factors in the model included those relating to the injured cyclist, the route environment, and involved motorists. Injury severity among cyclists was likely to be higher where an Heavy Goods Vehicle (HGV) was involved, and certain route conditions (bicycle infrastructure, 30 kph zones, and urban zones) were associated with lower injury severity. Interactions exist between the two: collisions involving large vehicles in lower-risk environments are less likely to lead to KSIs than collisions involving large vehicles in higher-risk environments. Finally, motorists involved in a collision were more likely than the injured cyclists to have committed an error or infraction. The study supports the creation of infrastructure that separates cyclists from motor traffic. Also, action needs to be taken to address motorist behaviour, given the imbalance between responsibility and risk.

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“…Many other studies employed other machine learning techniques to investigate crashes risk factors and predict injury severity levels. Some of these tools include Bayesian networks [27] modified neural network pruning algorithm N2PFA [28], recurrent neural networks [29], data clustering [30,31], and non-dominated Sorting Genetic Algorithm with a neural network classifier [32].…”

Section: Decision Trees and Artificial Neural Network Onmentioning

confidence: 99%

Investigating Key Factors Influencing the Severity of Drivers Injuries in Car Crashes Using Supervised Machine Learning Techniques

Aljarrah¹,

Khasawneh²,

Al-Omari³

2019

JESTR

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Limiting number of fatalities and reducing injury severity of car crashes is a continuing global concern. This study investigates crash risk factors including driver, vehicle, roadway, and crash characteristics role in determining injury severity levels encountered by drivers in fatal car crashes. Three types of supervised machine learning techniques were used; Classification and Regression Tree (CART), Artificial Neural Networks (ANN) and Multinomial Logistic Regression. The CART model was used to elect the most influential factors in determining drivers' injury severity levels. The ANN model was used to predict drivers' injury severity based on crash attributes. The logistic model was used to identify the effect of different crash attributes in distinguishing drivers' injury severity levels and for comparison purposes. Consequently, CART model resulted in six significant factors, these factors are: airbag deployment, seatbelt use, driver age, vehicle rollover, collision type, and vehicle model year. It was found that airbag deployment has a strong correlation with severe injuries and even fatalities. The use of seatbelts appears to reduce injuries and fatalities. Furthermore, elderly drivers, front to front collisions, vehicle rollover and older vehicles seem to cause more mortalities and injuries. On the other hand, according to the logistic model, all the crash attributes were found significant in distinguishing between drivers' injury severity levels except for the roadway functional system. However, the ANN model outperformed the CART model in terms of accuracy and stability. Further, both models seem to outperform the logistic regression model in terms of prediction accuracy.

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Data mining on road safety: factor assessment on vehicle accidents using classification models

Cited by 59 publications

References 7 publications

Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach

Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach

Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment

Investigating Key Factors Influencing the Severity of Drivers Injuries in Car Crashes Using Supervised Machine Learning Techniques

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