Leveraging Ant Colony and Particle Swarm Optimization Algorithms for Assessing the Transit-Oriented Development Potential: A Case Study of Dhaka, Bangladesh

This study explores transit-oriented development (TOD) in Dhaka City using optimization algorithms to provide urban planning and policy-making insights. The analysis examined the distribution of the TOD index values across the city and identified areas with varying levels of TOD potential. Two optimization algorithms, ant colony optimization (ACO) and particle swarm optimization (PSO), were employed to assess and compare the TOD index values. The results highlight the significance of transit infrastructure in promoting sustainable urban development, particularly in proximity to existing mass rapid transit (MRT) lines. PSO is more suitable for this study among the optimization algorithms because it offers a more precise TOD potential assessment. The findings suggest prioritizing investments in transit infrastructure and implementing TOD-friendly policies to foster sustainable urban growth and improve residents’ quality of life. Future studies can benefit from optimizing the algorithm parameters and incorporating real-world data to improve the accuracy of the TOD assessments.