A Metaheuristic Approach to Map Driving Pattern for Analyzing Driver Behavior Using Big Data Analysis

The modern-day influx of vehicular traffic along with rapid expansion of roadways has made the selection of the best driver based on driving best practices an imperative, thus optimizing cost and ensuring safe arrival at the destination. A key factor in this is the analysis of driver behavior based on driver activities by monitoring adherence to the features comprising the established driving principles. In general, indiscriminate use of features to predict driver performance can increase process complexity due to inclusion of redundant features. An effective knowledge-based approach with a reduced set of features can help attune the driver behavior and improve driving patterns. Hence, a Deep Mutual Invariance Feature Classification (DMIFC) model has been proposed in this study for predicting driver performance to recommend the best driver. To achieve this, first, the driver behavior is broken down into various features corresponding to a simulated driving dataset and subjected to preprocessing to reduce the noise and form a redundant dataset. Thereafter, a Mutual Invariance Scale Feature Selection (MISFS) filter is used to select the relational features by calculating the spectral variance weight between mutual features. The observed mutual features are promoted to create a dominant pattern to estimate the Max feature-pattern generation using Driver Activity Intense Rate (DAIR). The features are then selected for classification based on the DAIR weightage. Additionally, the Interclass-ReLU (Rectified Linear Unit) is used to generate activation functions to produce logical neurons. The logical neurons are further optimized with Multiperceptron Radial Basis Function Networks (MP-RBFNs) to enable better classification of driver features for best prediction results. The proposed system was found to improve the driver pattern prediction accuracy and enable optimal recommendations of driving principles to the driver.