Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

It has been observed that driver behavior a direct and considerable impact upon factors like fuel consumption, environmentally harmful emissions, public safety, making it key consideration of further research in order to monitor control such related hazards. This fueled our decision conduct study arrive at an efficient way analyzing the various parameters find ways means positively impacting behavior. ascertained behavioral patterns can significantly analysis traffic-related conditions outcomes. In cases, specific vehicular be detected data mined analyze spatial or temporal movement as well position/track prominent trends. seeks determine efficacy exercise whether employed help efficiently criteria for defining driver’s style. To end, pattern performance utilizes computer modeled application generating set classifications based on autonomous driving indicators are characteristic aggression. draw insights from behavior, is using categories data, instance, steering wheel’s angle, braking conditions, acceleration vehicle speed, etc. Unlike previously developed mechanisms system-based patterns, which were not very efficacious, this endeavor assimilates contemporary breakthroughs real-world scenario approaches classification methods. Based system capabilities desired outcomes, distinct strategies detect target case, neural network algorithms utilized intensive prevailing styles. proposed approach evaluated multiple determinants identifying The results experiment Python, indicated model question was successful achieving 90% accuracy terms logistic regression.