Traffic prediction in a bike-sharing system

Abstract

Bike-sharing systems are widely deployed in many major cities, providing a convenient transportation mode for citizens' commutes. As the rents/returns of bikes at different stations in different periods are unbalanced, the bikes in a system need to be rebalanced frequently. Real-time monitoring cannot tackle this problem well as it takes too much time to reallocate the bikes after an imbalance has occurred. In this paper, we propose a hierarchical prediction model to predict the number of bikes that will be rent from/returned to each station cluster in a future period so that reallocation can be executed in advance. We first propose a bipartite clustering algorithm to cluster bike stations into groups, formulating a two-level hierarchy of stations. The total number of bikes that will be rent in a city is predicted by a Gradient Boosting Regression Tree (GBRT). Then a multi-similarity-based inference model is proposed to predict the rent proportion across clusters and the inter-cluster transition, based on which the number of bikes rent from/ returned to each cluster can be easily inferred. We evaluate our model on two bike-sharing systems in New York City (NYC) and Washington D.C. (D.C.) respectively, confirming our model's advantage beyond baseline approaches (0.03 reduction of error rate), especially for anomalous periods (0.18/0.23 reduction of error rate).