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.

I. Introduction

In today's data-driven era, real-time data processing and analysis have become core requirements in many fields, especially in scenarios such as traffic monitoring, logistics management, and smart cities, where real-time processing of trajectory data has important application value. The trajectory data is inevitably affected by noise interference during the acquisition process. How to effectively perform denoising processing to improve the accuracy and reliability of the data has become an urgent technical problem to be solved. This article aims to propose a real-time streaming trajectory denoising technique based on the Apache Flink framework. The main contribution lies in the combination of Gaussian noise model and particle filtering algorithm, using HMM-PF (Hidden Markov Model Particle Filter) method to effectively remove noise from trajectory data. By converting real-time collected trajectory data into stream data and utilizing the efficient computing power of the Flink framework, fast processing and denoising of trajectory data can be achieved. This article discusses in detail the application of HMM-PF algorithm in state equations and observation equations, proposes an improved Bayesian filtering algorithm to optimize the state update process, and implements an efficient particle resampling mechanism in the Flink framework.