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

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.

Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads

“…Vehicles without transport qualifications do not meet the requirements for vehicle stability, braking, tank pressure resistance and impact, making them susceptible to leakage, fire or explosions. Road lights in rural areas are not well configured and have poor driving visibility in the early morning [28], and drivers are prone to fatigue, leading to a decrease in the perception of the surrounding environment and the ability to perform driving operations. The physical and chemical properties of different Hazmat differ greatly from each other, and the consequences of an accident are diverse and complex.…”

“…They found that bus drivers are at greater risk toward the middle of their shift, especially when in dense traffic. Yassin et al [28] used a hybrid k-means and random forest algorithm approach to road accident prediction and model interpretation. They found that driver experience and day, light condition, driver age, and service year of the vehicle were the decisive contributing factors for serious injury, light injury, and fatal severity, respectively.…”

Effect of Multi-Scale Decomposition on Performance of Neural Networks in Short-Term Traffic Flow Prediction

“…Non-parametric models have become the mainstream short-term traffic flow forecasting models due to their strong nonlinear fitting ability and self-learning characteristics. Decision tree models, including Random Forest (RF) [9] [10]and Gradient Boosting Decision Tree (XGBT) [11], are typical non-parametric models that are not prone to overfit and have a high anti-interference capacity. Support vector This work is licensed under a Creative Commons Attribution 4.0 License.…”

Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach

“…Another study using the Recurrent Neural Network (RNN), Multilayer Perceptron (MLP) and Bayesian Logistic Regression found that only the RNN provided good accuracy [ 22 ]. The study of the decision tree-based algorithm (Random Forest: RF), nonparametric learning method (K-Nearest Neighbor: KNN), and modified traditional statistical model (Regularized Logistic Regression Classifier: Logit) also reported promising results in predicting road-traffic severity [ 23 ]. Furthermore, a recent study using ML algorithms synergized with clustering techniques (e.g., Fuzzy C-Means-based Support Vector Machines and Neural Networks) also obtained good performance in terms of accuracy and F1 score [ 24 ].…”