Taxi pick-up areas recommendation based on the short-term taxi trajectory data can greatly reduce the system overhead and improve the efficiency of the recommendation

but it often has the problems of data sparseness. For this reason

a Latent Factor Model integrated with the geographic information

called GeoLFM

is put forward. This model makes up the faultiness of data sparseness by integrating the geographic information relating to drivers into the Matrix decomposition

which records the visiting relationship between drivers and pick-up areas. Meanwhile

different pick-up areas can be recommended for the taxis in various locations according to the real-time spatial context of the taxis. Experimental results show that

with the comparison between our proposed method and others

the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) between the recommended results and the actual value are significantly reduced

which indicates the recommendation effect is better improved.