Optimizing Bike Sharing System Flows Using Graph Mining, Convolutional and Recurrent Neural Networks

A Bicycle-sharing system (BSS) is a popular service scheme deployed in cities of different sizes around the world. Efficiently keeping bicycle-sharing system as balanced as possible is the main problem and thus, predicting or minimizing the manual transportation of bikes across the city is the prime objective in order to save logistic costs for operating companies. The purpose of this paper is two-fold: Identification of spatial structures and their structural change using Convolutional neural network (CNN) that takes adjacency matrix snapshots of unbalanced sub-graphs, and the Long short-term memory artificial recurrent neural network (RNN LSTM) in order to find and predict its dynamic patterns. As a result, we are predicting bike flows for each node in the possible future subgraph configuration, which in turn informs bicycle-sharing system owners to plan accordingly. Benefits are identified both for urban city planning and for bike-sharing companies by saving time and minimizing their cost.