Intelligent Taxi Dispatching Based on Improved Artificial Fish Swarm Algorithm

Taxi plays an important role in public transportation. However, in real life, it usually takes a long time for passengers to wait for an unoccupied taxi, while number of no-load taxi is very high. Optimized scheme is required to handle global taxi dispatching. The paper presents a novel method of taxi dispatching based on Artificial Fish Swarm Algorithm (AFSA). To avoid slow convergence and falling into local optimum, standard AFSA is improved using strategies of optimizing visual range, step size and swimming behavior. We compare our improved algorithm with standard AFSA and Particle Swarm Optimization through the evaluation with real data of GPS trajectory of 12,000 taxis in Beijing and make performance analysis of effects of parameter configuration to dispatching algorithm. The experimental results show good effectiveness of our method.