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The complexity of taxi operation system grows out of the inherent dynamics and randomness of taxi services. A simulation model of cruising taxi operation system is presented, through which, the dynamic features of the taxis 24-hour available service can be recurred. A new method of determining the system optimal taxi fleet size is developed. These are helpful to taxi supervisor for providing better service.

To circumvent the poor prediction accuracy of traffic volume models available due to the lack of traffic data and inaccurate judgments on the traffic influence factors, in this paper we established a traffic volume prediction model using grey forecasting model GM(1,1) based on the real traffic data from the highway toll database. The GM(1,1) method has advantage of the strong adaptiveness to Complex system, thus getting a great advantage over other methods for modeling such a complex nonlinear traffic volume system with many uncertain influence factors. Simulation results show that our GM(1,1) model has mean relative prediction error of 3.9%, which accomplishes our intended prediction accuracy.