Hybrid Approach for Driver Behavior Analysis with Machine Learning, Feature Optimization, and Explainable AI

TL;DR

This paper presents a hybrid driver behavior analysis model combining machine learning, feature optimization, and explainable AI to enhance accuracy and interpretability in road safety applications.

Contribution

It introduces a novel hybrid approach that integrates feature selection and explainability techniques with machine learning for driver behavior analysis.

Findings

Random Forest achieved 95% accuracy

Feature importance identified key drivers of behavior

Model accuracy remained high after optimization

Abstract

Progressive driver behavior analytics is crucial for improving road safety and mitigating the issues caused by aggressive or inattentive driving. Previous studies have employed machine learning and deep learning techniques, which often result in low feature optimization, thereby compromising both high performance and interpretability. To fill these voids, this paper proposes a hybrid approach to driver behavior analysis that uses a 12,857-row and 18-column data set taken from Kaggle. After applying preprocessing techniques such as label encoding, random oversampling, and standard scaling, 13 machine learning algorithms were tested. The Random Forest Classifier achieved an accuracy of 95%. After deploying the LIME technique in XAI, the top 10 features with the most significant positive and negative influence on accuracy were identified, and the same algorithms were retrained. The accuracy of the Random Forest Classifier decreased slightly to 94.2%, confirming that the efficiency of the model can be improved without sacrificing performance. This hybrid model can provide a return on investment in terms of the predictive power and explainability of the driver behavior process.