Safe Roads: Traffic Management and Road Safety Platform for Smart Cities

The escalating volume of traffic and the urgent need for enhanced road safety have emerged as critical concerns for both citizens and authorities. In this context, the concept of smart cities has proven to be a valuable resource, leveraging sensors to gather useful data and enabling comprehensive city monitoring. This study proposes the development of a traffic management and road safety platform that utilizes data from diverse sources. Our approach begins by employing cameras and the YOLOv8 object detection model to detect vehicles traveling in the wrong direction. Additionally, we trained a model using a labeled dataset and YOLOv8 to accurately identify potholes. To ensure a comprehensive view of various events within the city, the proposed system integrates data from HERE and OpenWeather, including traffic events, traffic flow, and weather data. By conducting thorough traffic flow analysis, we uncover a range of trends and seasonal patterns, leading to the development of two traffic forecasting models based on LSTM neural networks. These models provide valuable insights into future traffic patterns and aid in proactive planning and decision-making for efficient traffic management and improved road safety in smart cities.