Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach
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Road accident prediction and model interpretation using a hybrid K‑means and random forest algorithm approach Salahadin Seid Yassin1 · Pooja1 Received: 14 February 2020 / Accepted: 22 June 2020 © Springer Nature Switzerland AG 2020
Abstract 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. Keywords Clustering · Classification · Model interpretation · Hybrid model · Road safety
1 Introduction Road traffic accident (RTA) is churning the world with killing thousands and bringing demolition of property in a day without discrimination but did not give much attention to mitigate the severity. However, it is one of the life-threatening incidents in the world cause of death and property damage. Identifying the primary road traffic accident factors will help to provide an appropriate solution to minimize the adverse effect of severity on human and property loss. Road Severity does not occur by chance: It has patterns and can be predicted and avoided. So, accidents are “events which can be examined, analyzed, and prevented” [20]. According to workers’ health organization, accidents defined as “Fatalities are not fated; accident does
not just happen; illness is not random; they are caused [33]. Traffic accidents occurred daily in the capital city of Addis Ababa—Ethiopia. Human beings’ life and property damage with a fraction of seconds. It is one of the leading terrifying causes of death in the country. RTA severity is one of the research areas in these two decades in road safety. Researchers were using interesting methods on the road accident severity classification based models. The authors were studying using a traditional statistical-based approach for model building. These techniques help to get insights and identify th
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