TY - GEN
T1 - A Data-Driven Spatial-Temporal Graph Neural Network for Docked Bike Prediction
AU - Li, Guanyao
AU - Wang, Xiaofeng
AU - Njoo, Gunarto Sindoro
AU - Zhong, Shuhan
AU - Chan, S. H.Gary
AU - Hung, Chih Chieh
AU - Peng, Wen Chih
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Docked bike systems have been widely deployed in many cities around the world. To the service provider, predicting the demand and supply of bikes at any station is crucial to offering the best service quality. The docked bike prediction problem is highly challenging because of the complicated joint spatial-temporal (ST) dependency as bikes are picked up and dropped off, the so-called 'flows', between stations. Prior works often considered the spatial and temporal dependencies separately using sequential network models, and based on locality assumptions. Without sufficiently capturing the joint spatial and temporal features, these approaches are not optimal for attaining the best prediction accuracy. We propose STGNN-DJD, a novel data-driven Spatial-Temporal Graph Neural Network to solve the bike demand and supply prediction problem by unifiedly embedding the Dynamic and Joint ST Dependency in two novel ST graphs. Given station locations and historical rental data on bike flow over the past time slots 0 to t-1, we seek to predict online the bike demand and supply at any station at time t. To extract joint spatial-temporal dependency, STGNN-DJD employs a graph generator to construct, at the beginning of time t, two graphs that embed the flow relationships between stations at various time slots (flow-convoluted graph) and dynamic demand-supply pattern correlation between stations (pattern correlation graph), respectively. Given the two spatial-temporal graphs, STGNN-DJD subsequently employs a graph neural network with novel flow-based and attention-based aggregators to generate embedding of each station for docked bike prediction. We have conducted extensive experiments on two large bike-sharing datasets. Our re-sults confirm the effectiveness of STGNN-DJD as compared with other state-of-the-art approaches, with significant improvement on RMSE and MAE (by 20%-50%). We also provide a case study on dynamic dependencies between stations and demonstrate that the locality assumption does not always hold for a docked bike system.
AB - Docked bike systems have been widely deployed in many cities around the world. To the service provider, predicting the demand and supply of bikes at any station is crucial to offering the best service quality. The docked bike prediction problem is highly challenging because of the complicated joint spatial-temporal (ST) dependency as bikes are picked up and dropped off, the so-called 'flows', between stations. Prior works often considered the spatial and temporal dependencies separately using sequential network models, and based on locality assumptions. Without sufficiently capturing the joint spatial and temporal features, these approaches are not optimal for attaining the best prediction accuracy. We propose STGNN-DJD, a novel data-driven Spatial-Temporal Graph Neural Network to solve the bike demand and supply prediction problem by unifiedly embedding the Dynamic and Joint ST Dependency in two novel ST graphs. Given station locations and historical rental data on bike flow over the past time slots 0 to t-1, we seek to predict online the bike demand and supply at any station at time t. To extract joint spatial-temporal dependency, STGNN-DJD employs a graph generator to construct, at the beginning of time t, two graphs that embed the flow relationships between stations at various time slots (flow-convoluted graph) and dynamic demand-supply pattern correlation between stations (pattern correlation graph), respectively. Given the two spatial-temporal graphs, STGNN-DJD subsequently employs a graph neural network with novel flow-based and attention-based aggregators to generate embedding of each station for docked bike prediction. We have conducted extensive experiments on two large bike-sharing datasets. Our re-sults confirm the effectiveness of STGNN-DJD as compared with other state-of-the-art approaches, with significant improvement on RMSE and MAE (by 20%-50%). We also provide a case study on dynamic dependencies between stations and demonstrate that the locality assumption does not always hold for a docked bike system.
KW - bike demand and supply prediction
KW - spatial-temporal data prediction
KW - spatial-temporal graph neural network
UR - http://www.scopus.com/inward/record.url?scp=85136440797&partnerID=8YFLogxK
U2 - 10.1109/ICDE53745.2022.00058
DO - 10.1109/ICDE53745.2022.00058
M3 - Conference contribution
AN - SCOPUS:85136440797
T3 - Proceedings - International Conference on Data Engineering
SP - 713
EP - 726
BT - Proceedings - 2022 IEEE 38th International Conference on Data Engineering, ICDE 2022
PB - IEEE Computer Society
T2 - 38th IEEE International Conference on Data Engineering, ICDE 2022
Y2 - 9 May 2022 through 12 May 2022
ER -