Against the backdrop of global climate change, reducing carbon dioxide emissions from the transportation sector has become an urgent necessity. This study focuses on Wuhan City, constructing an urban road mobile monitoring system. By integrating data from greenhouse gas concentration sensors, GPS, meteorological sensors, panoramic cameras, and LiDAR measurement equipment, high-precision spatiotemporal matching is achieved, effectively eliminating discrepancies in spatial distribution and temporal points from single data sources, and ensuring the accuracy of urban road traffic greenhouse gas monitoring. The research results indicate that the CO2 concentration on roads exhibits significant tidal variation patterns. The average CO2 concentration on workdays (472 ppm) is higher than on non-workdays (460 ppm), with notable increases during morning and evening peak hours. The CO2 concentration in tunnels is significantly higher than in other road areas, with concentrations in the East Lake Tunnel, Yangtze River Tunnel, and ShuiGuo Lake Tunnel reaching 1201 ppm, 1046 ppm, and 1012 ppm, respectively, approximately twice the average. The CO2 concentration distribution on ring roads shows significant diurnal differences, with higher concentrations on the Second and Third Ring Roads during the day compared to the Fourth and Fifth Ring Roads, while at night, the Fourth and Fifth Ring Roads exhibit relatively uniform increases, slightly higher than the Second and Third Ring Roads. Based on these findings, this study proposes differentiated emission reduction strategies for central urban areas, new urban areas, and different time periods, providing scientific evidence and data support for the low-carbon transformation and sustainable development of transportation system.