TrafficPulse: A Road-Sensor Assisted Traffic Tweet Misinformation Detection System

Traffic incident detection is a well-established task in transportation, traditionally addressed using a combination of traffic sensors and driver reports. More recently, social media has become a rich data source for timely incident detection. However, the highly dynamic nature of social media, the challenges in mapping textual content to precise real-world locations, and potentially misleading posts complicate the extraction of reliable traffic information. Motivated by these challenges and leveraging recent advances in large language models (LLMs), we propose a real-time tweet validation pipeline that extracts and verifies traffic incidents reported on X (former Twitter). Our approach employs advanced parsing techniques for localization extraction. It integrates publicly available data to confirm the existence of an incident, thereby enhancing the robustness of downstream traffic analysis methods that combine sensor data with verified textual features. To support further research in this domain, we also introduce two new datasets: the Twitter Traffic Incidents dataset, which comprises manually curated and human-verified incident reports, and the PeMS Sensor + Incidents Reports dataset, featuring snapshots from California's PeMS traffic sensor system. Experimental results demonstrate that our pipeline significantly improves the reliability of traffic incident validation in tweets, serving as a basis for future traffic anomaly detection research.