Design of real-time fault detection and automatic recovery algorithm in intelligent transportation systems

With the rapid development of intelligent transportation systems, the complexity and real-time requirements of traffic management are increasing. Fault detection and automatic recovery, as an important part of intelligent transportation system, plays a crucial role in ensuring the efficient operation and safety of the system. The aim of this paper is to study a fault detection and automatic recovery algorithm based on real-time data analysis. This paper proposes a fault detection method that integrates big data analytics and machine learning, which is able to monitor the indicators of the transportation system in real time and quickly identify potential faults. For the demand of rapid response after the occurrence of faults, an automatic recovery algorithm is designed to ensure that the system can be restored to the normal operation state in the shortest possible time through intelligent scheduling and adaptive control mechanism. Experimental results show that the algorithm proposed in this paper is better than the traditional methods in terms of fault detection accuracy and recovery time, and can effectively improve the reliability and stability of intelligent transportation systems. This paper discusses the challenges and future development direction of the algorithm in practical application, which provides a theoretical basis and practical guidance for the optimization and innovation of intelligent transportation systems.