Artificial Immune System for Optimizing Public Bus Transportation Route During Peak and Off-Peak Hour

The increasing private car ownership has led to an overwhelming amount of vehicles on the road and has resulted in bad traffic conditions. The most effective way to reduce the number of vehicles on the road is to increase the use of public transport such as the bus. However, public trusts towards public transport have mostly been diminished due to poor punctuality and efficiency of the public transports to reach their destinations on time. There are several artificial intelligence approaches that have been proposed in the past by other researchers using optimization techniques such as Genetic Algorithm, Ant Colony Optimization, and Artificial Immune System to increase the efficiency of the public transportation route. In this study, we propose an efficient artificial intelligence approach to come out with an efficient transportation route. The Artificial Immune System (AIS) approach is chosen due to its adaptive and efficient characteristics which have been demonstrated in many problem domains, including transportation route optimization problems. Although there are few literatures available that focus on using AIS as a routing optimization technique, the results obtained from their studies have demonstrated that AIS is able to achieve better results than other methods. The algorithm will be tested with the Traveling Salesman Problem (TSP) benchmark dataset to validate its quality versus existing techniques. Moreover, the proposed AIS is conducted on a real case study of public bus transportation routes during peak and offpeak hour in Penang, Malaysia. The result of this study is expected to contribute towards the improvement of public bus transportation efficiency which would, in turn, encourage public usage as well as reduce the amount of vehicles on the road, thus minimizing bad traffic.