Dynamic Travel Path Optimization System Using Ant Colony Optimization

This paper demonstrates that ant colony optimization can efficiently improve the traffic situation in an urban environment. A Dynamic Travel Path Optimization System (DTPOS) based on Ant Colony Optimization (ACO) is proposed for the prediction of the best path to a given destination. In DTPOS, traffic factors such as average travel speed, average waiting time of cars and number of stopped cars in queue are taken into consideration. The proposed method is modeled in NetLogo. The simulation results demonstrate that the DTPOS model can greatly reduce the average travel time of cars in urban cases and improves the mean travel time by 47 percent when compared to similar models where the cars select their path without ACO. It has also been shown that the results can be further improved by 56 percent when the Previous Path Replacement (PPR) method is applied to the DTPOS results.