(untitled) (International Journal of Environmental Sciences, 2025)
In developing nations like India, where traffic is tremendous and constantly changing, traffic optimization is the largest issue. In contrast to Ant Colony Optimization or Inverted Ant Colony Optimization, which involved vehicle scheduling, this paper presents a unique Cheetah Optimization Technique that uses the Djkstra algorithm. Urban traffic congestion, longer commutes, and environmental degradation have become urgent issues due to the exponential expansion in vehicle numbers and fast urbanization in developing nations like India. Unlike traditional methods or Ant Colony-based systems, COA leverages high-speed decision-making and intelligent path selection to dynamically divert traffic away from congested areas. To evaluate the impact of COA in comparison to traditional shortest-path routing algorithms, a number of experiments were carried out using traffic simulation tools like SUMO (Simulation of Urban MObility) and including real Indian city data. The findings show that the COA-based method enhances network performance overall and dramatically lowers traffic congestion In addition, the algorithm promotes key sustainability goals: fuel consumption and CO₂ emissions dropped by nearly 45%, aligning with national urban mobility plans and India’s Smart City Mission objectives. The implementation of such bio-inspired optimization not only enhances individual commute experiences but also contributes to broader environmental and infrastructural resilience—making it highly relevant for developing urban ecosystems in India and other Global South contexts.