Core Viewpoint - The implementation of an innovative hotline service model in Wuhan has successfully addressed traffic congestion issues affecting over 400,000 residents, showcasing the effectiveness of integrating AI and data-driven approaches in public service [1][2]. Group 1: Traffic Congestion Solutions - The Huangxiaoh River Road is a critical traffic artery for over 400,000 residents, experiencing high vehicle flow and frequent accidents during peak hours, leading to numerous complaints to the Wuhan citizen hotline [1]. - The hotline introduced a new model combining "hotline-driven, data-enabled, and four-dimensional collaboration" to respond to public concerns, implementing a rapid response mechanism of "1-hour response, 24-hour resolution" [1][2]. - A "congestion heat map" was created by integrating hotline complaint data, traffic monitoring, and illegal parking records to identify specific congestion points for real-time police deployment and traffic signal adjustments [1]. Group 2: Collaborative Governance - The hotline conducted a comprehensive review of traffic complaints and organized discussions with local traffic management, residents, property companies, and educational institutions to develop a collaborative governance plan [2]. - The traffic team optimized parking lot traffic flow and improved vehicle passage efficiency by marking fire lane grids and enhancing management at the Tianhui Building entrance [2]. - Local schools and community organizations promoted staggered school release times and encouraged green travel to alleviate road congestion [2]. Group 3: Performance Improvement - Traffic flow resilience on Huangxiaoh River Road has significantly improved, with average speeds during peak hours increasing and travel times reduced by approximately 50%, while complaints decreased by about 88% [2]. - The Wuhan citizen hotline has transitioned from a traditional service to an intelligent service platform, utilizing AI for smart classification, dispatch, and closed-loop management, effectively addressing seasonal and regional public issues [2].