Hybrid golden jackal fusion based recommendation system for spatio-temporal transportation's optimal traffic congestion and road condition classification

Abstract

Traffic congestion, influenced by varying traffic density levels, remains a critical challenge in transportation management, significantly impacting efficiency and safety. This research addresses these challenges by proposing an Enhanced Hybrid Golden Jackal (EGJ) fusion-based recommendation system for optimal traffic congestion and road condition categorization. In the first phase, road vehicle images are processed using Enhanced Geodesic Filtering (EGF) to classify traffic density as heterogeneous or homogeneous across heavy, medium and light flows using Enhanced Consolidated Convolutional Neural Network (ECNN). Simultaneously, text data from road safety datasets undergo preprocessing through crisp data conversion, splitting and normalization techniques. This data is then categorized into weather conditions, speed, highway conditions, rural/urban settings and light conditions using Adaptive Drop Block Enhanced Generative Adversarial Networks (ADGAN). In the third phase, the EGJ fusion method integrates outputs from ECNN and ADGAN classifiers to enhance classification accuracy and robustness. The proposed approach addresses challenges like accurately assessing traffic density variations and optimizing traffic flow in historical pattern scenarios. The simulation outcomes establish the efficiency of the EGJ fusion-based system, achieving significant performance metrics. Specifically, the system achieves 98% accuracy, 99.1% precision and 98.2% F1-Score in traffic density and road condition classification tasks. Additionally, error performance like mean absolute error of 0.043, root mean square error of 0.05 and mean absolute percentage error of 0.148 further validate the robustness and accuracy of the introduced approach.