Abstract. This research work introduces the ant colony optimization (ACO) algorithm as an IT-Based applications used for route optimization on the Bus Rapid Transit (BRT) network which is actually an adaptation of the ACO implementation of the Traveling Salesman Problem. Each bus-stop is treated as a node with the routes and accompanying bus stops on the BRT master plan optimized. This would enable the Government know where to route future bus ways and deploy more buses. On the part of commuters, it would help them in choosing which route is the optimal to adopt in terms of getting to work on time. For the itinerant salesperson, it would help remotely in knowing which route to take in order to cover more or all the cities on the metropolis, and for the computer scientists, it would further expose them to the intricacies of algorithm design and implementation especially as obtained in a developed country. We are through with the algorithm design and in the coding and testing stage of our program. The Matlab programming language is being used for the coding process due to its high graphics and widespread applicability in solving optimization problems. The little problems we have experienced so far are in the areas of inadequate power supply, unavailability of high-speed computers and scarcity of research experts especially in the area of computer algorithms.
Keywords: Swarm Intelligence, ACO, BRT, Route Optimization, TSP for BRT problem
Ukaoha, K. C. and Chiemeke, S. C. (2010) Information Technology-Based Optimization of Commuter Transit System, Doctoral Consortium of the 3rd International Conference on ICT for Africa, March 25-27, Yaounde, Cameroon. Baton Rouge, LA: International Center for IT and Development.