A thorough understanding of the pollutant emission characteristics of heavy-duty diesel vehicles is of great significance for the upgrading of the city's mobile pollution source monitoring methods. This study obtains the actual road emission data and driving condition data of heavy-duty diesel vehicles in Qingdao through remote monitoring methods. By using big data analysis techniques, the impact of specific power on the NO_x and CO₂ emissions of diesel vehicles was analyzed, as well as the temporal distribution characteristics of pollutant emissions. The results show that the contribution rates of NO_x and CO₂ pollutants under acceleration, constant speed, deceleration, and idle conditions decrease in sequence. Under uniform acceleration conditions, the emission factors of NO_x and CO₂ are relatively high in the low-speed condition range, slightly decrease in the medium-speed condition range, and continue to decrease in the high-speed condition range. Appropriately increasing the average driving speed of heavy-duty diesel vehicles can reduce their NO_x and CO₂ emissions. The pollutant emissions of heavy-duty diesel vehicles show a trend of being higher during the day and lower at night, with the peak emission period of NO_x and CO₂ being from 5:00 to 8:00 every day.