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Generating travel behavior based on artificial population and an activity plan is a conventional method for traffic simulation. As a complicated and important constituent of travel behavior, destination selection is a decision-making process for space transfer and has been studied extensively in the disaggregate model. However, existing selection models only focus on the psychology or custom of individuals from a microscopic perspective and rarely take account of the actual traffic state. This causes a large deviation in simulation results and thus results in some obstacles for application. In this paper, a new destination selection model based on link flows is proposed. Further, a searching algorithm for an observed link set is given, and compressed sensing is used in the model solution. Experiments demonstrate that this model can predict the actual traffic state in rush hours quite well. Therefore, it contributes to the credible simulation and computational experiments.