Real-Time Traffic Management Using Cyber-Physical Systems and Machine Learning Techniques

Traffic management in real-time is a very challenging issue, especially among cities, due to lagging congestion and delays. Some new proposals aimed at addressing this problem are cyber-physical systems and machine-learning techniques. Physical Systems paired with Computation & Communication to Observe, Analyze, & Control traffic in realtime is a CPS. This allows for an abundance of data to be collected from sources like traffic cameras, sensors, and GPS devices. We can use machine learning techniques for these data mathematic and programming approaches that believe processes by learning's from new for automatic the construction automatically provide powerful profiled model traffic drivers flow. Such measures involve signal adjustments, changes to vehicle routes and the scheduling of public transportation. CPS can bring advanced traffic control by processing all real-time data and machine learning algorithms, which promptly respond to road conditions while matching dynamic situational changes as well. It can help us reduce the time spent traveling, enhance air quality, and improve road safety. Besides, the utilization of CPS and machine learning in traffic management can be a huge economic benefit as this will help reduce fuel consumption costs and pump productivity.