Sustainable transportation emission reduction through intelligent transportation systems: Mitigation drivers, and temporal trends

Intelligent Transportation Speed Guidance Systems (ITSGS) represent a burgeoning solution for sustainable emissions reduction. However, the absence of comprehensive environmental benefits assessment has impeded its advancement. This study analyzes how ITSGS achieves environmental benefits based on spatio-temporal data analysis. A fusion of machine learning-based emission models and extensive real-world trajectory data is utilized to quantify emissions. The K-Means clustering algorithm is employed to identify driving behavior. The results of the study show that by improving driving behavior, ITSGS can achieve emission reductions of 6.09 % to 9.24 % for CO2, 11.39 % to 18.17 % for NOx, 11.48 % to 18.04 % for co, and 3.84 % to 8.09 % for THC. At the same time, Furthermore, ITSGS consistently delivers significant environmental benefits across all time periods, with the most notable improvements occurring during off-peak hours. It also significantly reduces pollutant emissions in urban centers with high travel demand. Projections suggest that from 2025 to 2035, ITSGS will help China cumulatively avoid approximately 0.30 million tons of NOx, 3.31 million tons of CO, and 0.31 million tons of THC from light-duty passenger gasoline vehicles. This comprehensive environmental benefits assessment instills confidence in all transportation stakeholders that ITSGS can continue to contribute to green, low-carbon transportation.