Analysis of road traffic fatal accidents using data mining techniques

Li, Liling; Shrestha, Sharad; Hu, Gongzhu

doi:10.1109/sera.2017.7965753

Cited by 80 publications

(42 citation statements)

References 5 publications

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“…After prepossessing the study applied clustering Association rule and Nave Bayes to get trends of fatal accidents in the USA. The study explained and identify human and collusion types were the main cause of the fatality rate [34]. Other studies conducted using clustering and classification techniques to predict an accurate model in Iran.…”

Section: Litrature Reviewmentioning

confidence: 99%

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: Litrature Reviewmentioning

confidence: 99%

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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“…One way of classification is to determine the posterior probability for each class and assign c to the greatest probability class. [3]. Liling Li, Sharad Shrestha, Gongzhu Hu, "Analysis of Road Traffic Fatal Accidents Using Data Mining Techniques", 2017 IEEE 15th International Conference on Software Engineering Research Management and Applications (SERA).…”

Section: Related Workmentioning

confidence: 99%

Research on Road Traffic Fatal Accidents using Data Mining Techniques

2019

IJITEE

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Road safety plays a major role in our day-to-day life and also transportation system, due to its priority, it has become the major concern for everyone. In order to increase the road safety, traffic rules are included in education, clear and careful predictive analysis and study is done on factors effecting fatal accidents. We apply predictive analysis, statistical analysis and some algorithms related to data mining which includes FARS such as Apriori algorithm, associative rule techniques are used. These methods help in encountering the road fatal accidents that cause due to mentioned factors. These factors may include climatic and surface conditions and also drunken drivers or may be condition of vehicles also. Clusters are formed using simple k-means clustering algorithms. Finally road safety driving rules are made based on the factors effecting, clusters formed and predictive analysis and prior information.

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“…The signal processing methods enable us to treat these irregular days. Li et al analysed road traffic data to find out variables closely related to fatal accidents [10]. They investigated the relationship between fatal rate and attributes including weather, surface condition, and light condition.…”

Section: Considerationsmentioning

confidence: 99%

Estimation of Daily Life Time Series Data Affected by Rainfall

Hochin¹,

Nomiya²

2018

IJET

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The amount of sewage flow, which is one of daily life data, was estimated for their efficient management. The amounts of flow of a typical day were tried to be adjusted to those of a day. The values for the adjustment were tried to be estimated by using the multiple regression analysis. This method is applied to the estimation of the ammonia nitrogen concentration, which is the major factor of the quality of sewage flow. The estimation results show that this method is applicable to the estimation of the ammonia nitrogen concentration, and that the amount of rainfall is dominant in estimating the ammonia nitrogen concentration.

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Analysis of road traffic fatal accidents using data mining techniques

Cited by 80 publications

References 5 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

Research on Road Traffic Fatal Accidents using Data Mining Techniques

Estimation of Daily Life Time Series Data Affected by Rainfall

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