A Two-Stage Destination Prediction Framework of Shared Bicycles Based on Geographical Position Recommendation

In this study, we investigate the destination prediction problem for the trips of shared bicycles, which can assist authorities in overcoming the existing challenges like indiscriminate parking, bicycle rebalancing, etc. An integral part of E-business, the recommendation system can predict the buyers' needs and recommend personalized contents for each buyer. Likewise, prediction of travelers' destinations is essentially a geographical position recommendation. In this paper, we present a two-stage destination prediction framework inspired by the recommender system. It is an innovative framework for the destination prediction problem in transportation studies. The architecture is highly flexible and extendable while it utilizes the user's historical data such as departure time, geographic position, and traveling behavior. The experiments demonstrate that the proposed method performs well in the real situation.