Freeway Traffic Conflict Forecasting: A Machine Learning Approach with RF-LSTM Integration

This paper aims to forecast and examine traffic conflicts by integrating Random Forest (RF) alongside Long Short-Term Memory Network (LSTM). The paper begins with the Random Forest method, pinpointing essential elements affecting traffic conflicts, revealing that the speed difference between interacting vehicles and their leaders, as well as the average headway and distance have significant effects on the occurrence of traffic conflicts. The forecasted Time to Collision (TTC) metric demonstrates extraordinary accuracy, confirming the creation of a precise traffic conflict forecast model. The model expertly predicts the vehicle's trajectory. This model skillfully anticipates vehicle paths and potential traffic conflict, demonstrating strong alignment with actual traffic patterns and offering support for traffic management by highlighting imminent risks. Merging RF with feature selection and LSTM for temporal dynamics enhances the forecasting capability. Furthermore, it also illuminates changes in traffic interaction patterns. Considering both fixed and shifting elements, this extensive process leads to a deep understanding of the subtle mechanisms driving traffic conflicts. The suggested platform serves as a robust device for traffic engineers and policymakers, enabling them to make informed decisions and implement effective strategies for managing traffic.