Solving the dynamic shortest path problem has become important in the development of intelligent transportation systems due to the increasing use of this technology in supplying accurate traffic information. This paper focuses on the problem of finding the dynamic shortest path from a single source to a destination in a given traffic network. The goal of our studies is to develop an algorithm to optimize the journey time for the traveler when traffic conditions are in a state of dynamic change. In this paper, the models of the dynamic traffic network and the dynamic shortest path were investigated. A novel dynamic shortest path algorithm based on hybridizing genetic and ant colony algorithms was developed, and some improvements in the algorithm were made according to the nature of the dynamic traffic network. The performance of the hybrid algorithm was demonstrated through an experiment on a real traffic network. The experimental results proved that the algorithm proposed in this paper could effectively find the optimum path in a dynamic traffic network. This algorithm may be useful for vehicle navigation in intelligent transportation systems.