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Bike flow prediction with multi-graph convolutional networks

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Traffic Prediction and Management Techniques Urban Transport and Accessibility Advanced Graph Neural Networks

Abstract

One fundamental issue in managing bike sharing systems is bike flow prediction. Due to the hardness of predicting flow for a single station, recent research often predicts flow at cluster-level. However, they cannot directly guide fine-grained system management issues at station-level. In this paper, we revisit the problem of the station-level bike flow prediction, aiming to boost the prediction accuracy using the breakthroughs of deep learning techniques. We propose a multi-graph convolutional neural network model to predict flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective. More specifically, we construct multiple graphs for a bike sharing system to reflect heterogeneous inter-station relationships. Afterward, we fuse multiple graphs and apply the convolutional layers to predict station-level future bike flow. The results on realistic bike flow datasets verify that our multi-graph model can outperform state-of-the-art prediction models by reducing up to 25.1% prediction error.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2015Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network 2015Traffic prediction in a bike-sharing system 2016Dynamic cluster-based over-demand prediction in bike sharing systems

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2021Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting 2020Urban flow prediction from spatiotemporal data using machine learning: A survey 2020Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction 2022Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities 2020How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey 2020

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