This research paper delves into the potential of our proposed AI model, the Information Extraction System (IES), for extracting traffic-related information from image sources. It is based on the performance of Transformer models and addresses the challenges and potential solutions in these areas, with a focus on responsible AI deployment. The paper also outlines future research and development directions, such as integrating multi-modal information, providing real-time updates, and enhancing user interaction, 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 assessed by comparing the extracted information with ground truth data, and it remained consistent throughout the validation process.