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To overcome the problem that the existing short-term parking demand model cannot be directly used for airport parking demand prediction, a short-term parking demand prediction model for airport parking lots was proposed based on parking data, flight schedules, and weather data. Firstly, the parking data was used to analyze the short-term vehicle arrival and departure characteristics. Then taking the impact of flight schedules and the weather on the short-term parking demand of airport parking lots, an airport short-term parking demand prediction model using the Conv1 D-long short-term memory (LSTM) neural network was established. Taking the Shanghai Hongqiao Airport parking lots as an example, the mean absolute error and root mean square error of the Conv1 D-LSTM model are 12.057 and 14.237 vehicles, respectively. Compared with several other models, the proposed Conv1 D-LSTM model has a better prediction performance and can be effectively used for short-time parking demand prediction in airport parking lots. [ABSTRACT FROM AUTHOR]

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