With the acceleration of China's urbanization process, the phenomenon of urban road traffic congestion is becoming increasingly serious, and the problem of road bottlenecks is becoming increasingly prominent. The operational efficiency of urban roads has encountered challenges. The rapid development of intelligent technologies such as artificial intelligence, big data, and the Internet of Things has provided more possibilities and opportunities for the research of digital highways. These technologies can be applied to traffic flow monitoring, road condition prediction, intelligent traffic management, and other aspects to improve highway traffic efficiency and service quality. This article designs a digital highway system based on intelligent technology to alleviate traffic congestion and other issues. Firstly, traffic information is collected and tested through channels such as circular coil detectors and cameras. This article would integrate and store the collected data into the Redis database; Then, the data is analyzed, processed, and predicted using the Kalman filtering algorithm. It then sends the predicted information to the client, providing convenience for users to choose the best route. This article tests the performance of the system through experiments. The experiment shows that the system can make correct analysis and prediction even in high pedestrian traffic, find the fastest driving route, and effectively resist the impact of fluctuations in pedestrian traffic. In addition, the system has achieved good results in accuracy testing of analysis and prediction. In summary, the digital highway system based on intelligent technology is expected to improve traffic efficiency, enhance traffic safety, achieve intelligent travel services, promote sustainable transportation development, and also bring broad prospects for commercial innovation and market opportunities. With the continuous progress and application of intelligent technology, digital highway systems would become an important tool for future road traffic to respond to challenges and improve service quality.
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