Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach

by   Lei Lin, et al.

Bike sharing is a vital piece in a modern multi-modal transportation system. However, it suffers from the bike unbalancing problem due to fluctuating spatial and temporal demands. Accurate bike sharing demand predictions can help operators to make optimal routes and schedules for bike redistributions, and therefore enhance the system efficiency. In this study, we propose a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model to predict station-level hourly demands in a large-scale bike-sharing network. With each station as a vertex in the network, the new proposed GCNN-DDGF model is able to automatically learn the hidden correlations between stations, and thus overcomes a common issue reported in the previous studies, i.e., the quality and performance of GCNN models rely on the predefinition of the adjacency matrix. To show the performance of the proposed model, this study compares the GCNN-DDGF model with four GCNNs models, whose adjacency matrices are from different bike sharing system matrices including the Spatial Distance matrix (SD), the Demand matrix (DE), the Average Trip Duration matrix (ATD) and the Demand Correlation matrix (DC), respectively. The five types of GCNN models and the classic Support Vector Regression model are built on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNN-DDGF model has the lowest Root Mean Square Error, followed by the GCNN-DC model, and the GCNN-ATD model has the worst performance. Through a further examination, we find the learned DDGF captures some similar information embedded in the SD, DE and DC matrices, and it also uncovers more hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.


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