Abstract
For a better driving experience and fewer traffic accidents, road surface condition is critical. Traditional road condition monitoring systems lack the temporal (speed) and geographical (coverage) responses required for overall road quality maintenance. Several alternative methods that use sensors installed on cars have been presented. To ensure road safety, the safe system approach focuses on designing safe cars, road networks, and road users. As a result of the World Health Organization (WHO), this technique is becoming increasingly popular across the world. In this research study, a short-to-medium term dynamic evaluation of road safety is proposed. Here, we provide an innovative, cost-effective Internet of things (IoT) architecture that allows the creation of a robust and dynamic computing core for evaluating the safety of road networks and their elements. The machine learning technique described here may be used to monitor roads for problems that pose a safety concern to commuters and to give maintenance information to appropriate authorities on a bigger scale.