Author(s): Nancy Elena*
In contemporary urban landscapes, the coexistence of diverse transportation modes within mixed traffic scenarios poses formidable challenges for mitigating emissions and optimizing traffic flow. This abstract explores the innovative approach of the Intersection Control Code (ICC), a dynamic traffic management system designed to address these challenges. Unlike conventional signal systems, the ICC employs real-time data analytics and adaptive algorithms to dynamically adjust signal timings based on vehicle density, transportation modes, and air quality parameters. By prioritizing sustainable modes, integrating with Intelligent Transportation Systems, and monitoring emissions, the ICC aims to reduce pollution, enhance traffic efficiency, and promote a greener urban environment. This abstract provides a concise overview of the key features and benefits of the ICC, highlighting its potential as a transformative solution for emission reduction in mixed traffic scenarios. Heavy-duty vehicles contribute to most of the emissions regardless of the low proportion in the traffic flows. Afterward, a model is proposed for determining the optimal signal control at an intersection for a specific percentage of heavy-duty vehicles based on the conversion of emission factors of different types of vehicles. Signal control is also optimized based on conventional signal timing, and vehicle emissions are calculated. In the empirical analysis, the changes in CO, HC, and NOx emissions of light- and heavy-duty vehicles before and after conventional signal control optimization are quantified and compared. After the signal control optimization, the CO, HC, and NOx emissions of heavy-duty vehicles were reduced. The CO and HC emissions of light-duty vehicles were reduced, but the NOx emissions of light-duty vehicles remained unchanged.
The Journal of International Social Research received 7760 citations as per Google Scholar report
