Heuristic Algorithm-Based Energy Optimization In Transportation To Reduce Carbon Emission

Abstract

India's transportation industry is entering a stage of high-quality development which has increased the quantity of carbon emissions and greenhouse gas (GHG) production. This is effecting the lives of millions of people in the rural as well as urban areas. In order to protect the environment and the health of people, government is focusing on the implementation of green and low-carbon transportation system which could reduce carbon emission to a significant extent. This paper thoroughly analyzes the main sources contributing to carbon emission in the transportation industry such as coal, gasoline, and diesel and predict their carbon emissions. It also focuses on the count of vehicles in 2023 which release significant amount of carbon in the environment. Particle Swarm Optimization (PSO) as a heuristic optimization technique was used to determine the optimal shift in vehicle population to EVs, aiming to minimize overall carbon emissions. The algorithm was used to find the optimal shift percentages for different vehicle types (two-wheelers, cars, goods vehicles, buses) towards electric vehicles to minimize carbon emissions. Results indicated that the PSO model has the highest degree of fit between the predicted value and the true value, which further illustrates the carbon emissions in the transportation industry. The PSO‐optimized scenario yields a dramatic CO₂ cut of ~71%, with the total road transport CO₂ falls from a baseline of 100.0 Mt to about 28.9 Mt. Each two-wheelers and cars drive large percentage drops, and even heavy trucks and buses showed substantial reductions in absolute terms. Accelerating the upgradation of the transportation structure by introducing green and low-carbon emission transportation system will be important measures to control carbon emissions and promote the sustainable development of the conveyance system.