A Destination Prediction Network Based on Spatiotemporal Data for Bike-Sharing

Algorithm 1

Candidate generation algorithm.

Input: Training set

Output: Candidate Set

1:Initialize the time ranges in training set

2:Select the data items from training set at time

3:Get according to users, origins and destinations with FPG at minimum

support

4:Get according to users and origins with FPG at minimum support

5:Get according to users and destination positions with FPG at minimum

support

6:Get according to origins and destination positions with FPG at minimum

support

7:Get the final candidates

8:Return the candidate set