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To solve the problem that the influencing factors of the airport bus operation process are complicated and the operation time is difficult to predict, this paper established an airport bus operation time prediction model based on the subspace identification algorithm. Firstly, based on the multi-source big data generated during the operation process, considering the number of passengers, departure interval, road congestion and other factors at different times, it established the state space model of the airport bus operation. Then it extracted the characteristic variables suitable for describing the operation process of the airport bus as the input and output of the model,and solved the model by subspace identification method. Finally,this paper took an actual operating route of the capital airport bus as a case for simulation analysis. The calculation results show that the mean absolute percentage error and mean square error of the model are 2. 25% and 4. 77, respectively, which are better than the traditional BP neural network prediction model and the least square identification model. The model has good prediction accuracy and has certain practical application value. [ABSTRACT FROM AUTHOR]

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