Traffic prediction in a bike-sharing system

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How researchers see this paper

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Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting (2017, methodology): “In order to eliminate atypical trafﬁc, only workday trafﬁc data are adopted in our experiment [Li et al. , 2015].”
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017, background): “Recently, researchers have started to focus on city-scale traffic flow prediction [20,27].”
Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach (2017, background): “As pointed out by many previous studies (Chen et al., 2016; Li et al., 2015; Lin, 2018; Zhou, 2015), it is common for BSSs with fixed stations that some stations are empty with no bikes to check out while others are full precluding bikes from being returned at those locations.”
Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning (2020, background): “researchers have started to focus on city-scale traffic flow prediction [14], [21].”
DNN-based prediction model for spatio-temporal data (2016, background): “There are many different types of measurements in a region for different ST applications, such as crowd flows [1], air quality [5], bike rent/return [3].”