Traffic prediction in a bike-sharing system

Yexin Li, Yu Zheng, Huichu Zhang, Lei Chen
2015 Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '15  
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
more » ... 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 bikesharing systems in New York City (NYC) and Washington D.C. (D.C.) respectively, confirming our model's advantage beyond baseline approaches (a 0.03 reduction rate on error), especially for anomalous periods (a 0.18/0.23 reduction rate on error).
doi:10.1145/2820783.2820837 dblp:conf/gis/LiZZC15 fatcat:ps6z7usmvfht5ncovw7eebjcsm