Efficient Trajectory Data Denoising Technique Combining Flink Framework and Hidden Markov Model Particle Filter (HMM-PF)

With the development of intelligent transportation systems and the Internet of Things (IoT), the acquisition and analysis of trajectory data have gained importance. However, trajectory data is often affected by noise, reducing its quality and impacting analysis. Traditional denoising methods, mostly based on simple filtering techniques, have limited processing power and struggle in dynamic environments. This paper proposes an efficient trajectory denoising method combining the Apache Flink framework with the Hidden Markov Model Particle Filter (HMM-PF), addressing the shortcomings of current approaches. By utilizing Flink's real-time stream processing and the HMM-PF model, the dynamic characteristics of trajectory data and noise uncertainty are better handled, improving denoising accuracy. Experimental results show that the HMM-PF model reduced the average processing time from 257.9ms to 155.2ms compared to traditional methods. This method provides a promising solution for trajectory data denoising, with broad application potential in intelligent transportation and logistics.