Towards an Approach of Traffic Information Extraction Through ChatGPT

This research paper explores the potential of our proposed AI model, known as Information Extraction System (IES), in extracting traffic-related information from textual sources. It is based on the performance of NLP models and discusses the challenges and potential solutions in these areas, with an emphasis on responsible AI deployment. Future directions for research and development are outlined, including integrating multi-modal information, providing real-time updates, and enhancing user interaction. These directions aim to enhance the capabilities and practical applicability of IES in traffic management systems. The findings have significant implications for the transportation industry, offering potential improvements in traffic monitoring, incident detection, and congestion management. Overall, this research contributes to the development of intelligent traffic management systems that leverage conversational AI models like ChatGPT, enabling enhanced efficiency and improved transportation experiences. The accuracy of our system was evaluated by comparing the extracted information with ground truth data, achieving an overall accuracy of 90%.