TY  - JOUR
AU  - Armenta-Déu, Carlos
PY  - 2025
DA  - 2025/12/03
TI  - GHG Emissions Management in Urban Areas by Interactive Road Traffic Control: Application of Artificial Intelligence
JO  - Advances in Environmental and Engineering Research
SP  - 033
VL  - 06
IS  - 04
AB  - The rising pollution levels in congested urban areas pose a serious threat to human health. Many solutions have been proposed to control and reduce gas emissions from road traffic, with results that are not entirely satisfactory. The adoption of new techniques and methodologies for early detection of pollutant emissions may enable effective management and control of road traffic contamination. The proposed method introduces an interactive protocol that facilitates communication between the traffic control center and a vehicle speed recognition system. This protocol autonomously adjusts vehicle speeds to align with traffic control center limits, correlating maximum velocity with greenhouse gas (GHG) concentrations in designated urban areas. The paper examines the environmental impacts of road traffic emissions in urban areas and explores strategies to mitigate them through speed limit management based on pollution levels. To implement this strategy, we suggest installing pollution detection sensors and interactive speed signage. GHG mitigation hinges on specific sensors that measure emissions from vehicle exhaust pipes strategically positioned along roads or pedestrian walkways. The integration of Artificial Intelligence enhances the protocol’s effectiveness, reducing significant deviations in vehicle performance and associated emissions. Simulation results indicate a potential reduction in GHG emissions of 21.8% when vehicles operate at 45 km/h, following a 15 km/h decrease from typical velocities and maintaining no acceleration. Additionally, vehicle speed, the extent of speed reduction, and acceleration rates influence the reductions in GHG emissions, yielding reference emission reductions of 12.4% and 15.2% for average speeds of 70 km/h and 45 km/h, respectively. Conversely, the maximum increase in GHG emissions can reach 66.7% when experiencing an acceleration of 3.6 m/s2, with a 15 km/h speed reduction. Theoretical predictions have demonstrated a high accuracy of 98.5% for speeds exceeding 57.5 km/h, regardless of the engine type. However, this accuracy diminishes at lower speeds (below 55 km/h), ranging from 69.5% to 98.5%, depending on engine type and acceleration rate. The proposed methodology improves the current state of the art in managing pollution levels and helps achieve early control of urban contamination from road traffic.
SN  - 2766-6190
UR  - https://doi.org/10.21926/aeer.2504033
DO  - 10.21926/aeer.2504033
ID  - Armenta-Déu2025
ER  -