Probabilistic forecasting and data reliability assessment in intelligent transportation systems

Probabilistic models for forecasting and assessing the reliability of navigation parameters in intelligent transportation systems are proposed. The relevance of the study is driven by the need to enhance the reliability of robotic transportation systems operating in dynamically changing urban environments. In such environments, sensor failures, signal distortions, and a high degree of data uncertainty are possible. The proposed approach is based on the application of probabilistic analysis methods and statistical control to detect anomalies in navigation parameters such as coordinates, speed, and orientation. The concept of navigation data reliability is introduced as a quantitative measure characterizing the degree of correspondence between the measured parameters and the actual state of the system. Key validity criteria are defined: confidence probability, significance level and confidence coefficients. To improve the reliability of parameter assessment, a combination of statistical analysis methods and filtering algorithms is proposed. Forecasting involves preliminary data processing aimed at smoothing noise and verifying data consistency. Outlier detection is performed using statistical methods, including confidence intervals and variance minimization. An forecasting model based on the Kalman filter and dynamic updating of probabilistic estimates has been developed. The integration of various methods into a unified system minimizes the impact of random and systematic errors, ensuring more accurate assessment of navigation parameters. The proposed approach is applicable to the development of navigation systems for autonomous robots and unmanned vehicles, enabling them to adapt to external conditions without the need for precise a priori data.