In order to meet the demand of people's travel, all kinds of hailing-taxi apps have emerged. Although the advent of these apps brings people convenience greatly, their shortcomings and deficiencies are also increasingly apparent. Due to the subjectivity of the taxi drivers, some drivers may pick up a passenger without considering the problem of traffic congestion, which decrease the efficiency of taxis. So an efficient coordination of taxi network at a large scale becomes a new challenge in the intelligent transportation. In order to better solve the insufficiency of taxi-hailing apps, strengthen the passengers' experience and improve the efficiency of taxi service, an intelligent recommendation service for taxi based on real-time traffic in the road network is designed. And then a global optimization algorithm based on simulated annealing particle swarm optimization is presented to search a vacant taxi which can reach the location of the passenger in the shortest time. The comparative experiments with other algorithms prove that the algorithm has certain effectiveness and efficiency.