Abstract
Vehicle routing in emerging cities, particularly in sub-Saharan Africa, relies on informal services such as minibus taxis. These systems often lack route optimization and planning, resulting in inefficiencies. This study introduces the design of a swarm intelligence-inspired routing algorithm, utilizing ant colony optimization (ACO), to improve the efficiency of the minibus taxi system in an emerging city. The proposed routing algorithm introduces four component units, namely: the ant-like agent, dynamic passenger demand, the traffic network (the environment), and legal routes. Critical challenges, including dynamic passenger demand, vehicle routing, and traffic and weather conditions, are explored. The algorithm enables continuous optimization of routes, reduction of travel times and improved overall efficiency by collecting and processing real-time data. By utilizing real-time data for predictive capabilities, the algorithm enables optimized route selection that is adaptable to changing traffic and passenger demands. Expert feedback highlights the completeness, adaptability, scalability, and predictive capabilities of the algorithm. Findings reveal that while the algorithm effectively optimizes minibus taxi routing efficiency, future studies should prioritize practical implementation to validate the algorithm’s effectiveness in real-world settings. Furthermore, continuous improvements in areas of integration, security, and user adoption are necessary to enhance the algorithm’s operational resilience and ensure sustainable impact within the public transportation sector in emerging cities.