Research on traffic congestion prediction based on analyzable machine learning

As motor vehicles proliferate around the world, traffic congestion increases, hindering travel, polluting the environment and hampering economic growth. Accurate congestion prediction is the key to optimizing traffic flow. In this study, the interaction between different traffic modes and congestion degree is deeply investigated, and a high-fidelity prediction model is established. The study analyses monthly, weekly, daily and hourly multi-modal traffic and congestion data and identifies congestion patterns. In this paper, Pearson's coefficient is applied to identify the key influencing factors, and congestion is predicted by KNN, logistic regression, decision tree and random forest models. The random forest model reaches 99.88% accuracy after 5-fold cross-validation and grid search optimisation. Further analysis using the Random Forest's feature importance scores revealed that traffic volume was the most important predictor of congestion, while time factors such as time and date played a smaller role. This study not only establishes a robust congestion prediction model, but also emphasizes the importance of traffic volume in congestion prediction. The findings provide valuable insights for traffic management agencies to take proactive measures to alleviate traffic congestion. By harnessing the predictive power of random forest models, these agencies can increase the efficiency of traffic management, improve the overall travel experience, and contribute to sustainable urban development. This study also lays a solid foundation for future research in the field of traffic congestion prediction and management.