  This study introduces an innovative methodology for the planning of metro
network routes within the urban environment of Chennai, Tamil Nadu, India. A
comparative analysis of the modified Ant Colony Optimization (ACO) method
(previously developed) with recent breakthroughs in nature-inspired algorithms
demonstrates the modified ACO's superiority over modern techniques. By
utilizing the modified ACO algorithm, the most efficient routes connecting the
origin and destination of the metro route are generated. Additionally, the
model is applied to the existing metro network to highlight variations between
the model's results and the current network. The Google Maps platform,
integrated with Python, handles real-time data, including land utilization,
Geographical Information Systems (GIS) data, census information, and points of
interest. This processing enables the identification of stops within the city
and along the chosen routes. The resulting metro network showcases substantial
benefits compared to conventional route planning methods, with noteworthy
enhancements in workforce productivity, decreased planning time, and
cost-efficiency. This study significantly enhances the efficiency of urban
transport systems, specifically in rapidly changing metropolitan settings such
as chennai.
