Abstract: A deep learning based method for integrating demand forecasting and scheduling of online ride-hailing at a hub is provided. The method includes: S1, performing data processing: performing missing value filling, outlier processing and normalization processing on the historical orders of online ride-hailing and relevant feature data of the urban transportation hub; S2, performing feature screening: primarily screening the relevant features by means of a Pearson correlation test and box plot analysis, calculating an influence degree of data of each feature on the orders of online ride-hailing by using an XGBoost algorithm, and secondarily screening the features; S3, performing model construction: constructing an integrated model for demand forecasting and scheduling decision of online ride-hailing at the urban transportation hub; and S4: performing algorithm design: designing a decision tree and deep learning combination algorithm, and calculating the number of online hailed rides to be scheduled.