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Predicting Short-Term Bike-Sharing Demand at Station Level: A Deep Graph-Based Multi-Task Spatiotemporal Approach

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Support-Vector Networks
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Bike-share rebalancing strategies, patterns, and purpose
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Exact loading and unloading strategies for the static multi-vehicle bike repositioning problem
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Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
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Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
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A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system
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Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network
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Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach
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Improving short-term bike sharing demand forecast through an irregular convolutional neural network
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Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem
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Short-term prediction for bike-sharing service using machine learning
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Deep learning methods in transportation domain: a review
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Forecasting usage and bike distribution of dockless bike‐sharing using journey data
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Short-Term Prediction of Traffic Volume in Urban Arterials
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Dynamic linear models to predict bike availability in a bike sharing system
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A Deep Learning Based Multi-Block Hybrid Model for Bike-Sharing Supply-Demand Prediction
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Travel Demand Prediction using Deep Multi-Scale Convolutional LSTM Network
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Understanding and Predicting the Short-Term Passenger Flow of Station-Free Shared Bikes: A Spatiotemporal Deep Learning Approach
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BikeNet: Accurate Bike Demand Prediction Using Graph Neural Networks for Station Rebalancing
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Travel-Time Prediction With Support Vector Regression
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Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks
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Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems
inferredcites →https://doi.org/10.1109/tits.2019.2900481openalex-snapshot
Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction
inferredcites →https://doi.org/10.1109/tits.2019.2909571openalex-snapshot
Short-Term Prediction of Passenger Demand in Multi-Zone Level: Temporal Convolutional Neural Network With Multi-Task Learning
inferredcites →https://doi.org/10.1109/tits.2020.3015542openalex-snapshot
Taxi Demand Prediction Using Parallel Multi-Task Learning Model
inferredcites →https://doi.org/10.1109/tits.2021.3080511openalex-snapshot
MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis
inferredcites →https://doi.org/10.1109/tits.2021.3097240openalex-snapshot
Short-Term Forecast of Bicycle Usage in Bike Sharing Systems: A Spatial-Temporal Memory Network
inferredcites →https://doi.org/10.1109/tkde.2019.2898831openalex-snapshot
Citywide Bike Usage Prediction in a Bike-Sharing System
inferredcites →https://doi.org/10.1109/tkde.2019.2948005openalex-snapshot
Multi-level Attention Networks for Multi-step Citywide Passenger Demands Prediction
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A Survey on Multi-Task Learning
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Prediction of Station Level Demand in a Bike Sharing System Using Recurrent Neural Networks
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Using LSTM and GRU neural network methods for traffic flow prediction
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Traffic prediction in a bike-sharing system
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot
XGBoost
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Dynamic cluster-based over-demand prediction in bike sharing systems
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Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems
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Bike flow prediction with multi-graph convolutional networks
inferredcites →https://doi.org/10.1145/3292500.3330887openalex-snapshot
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network
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Long Short-Term Memory
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Use of Deep Learning to Predict Daily Usage of Bike Sharing Systems
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Travel Time Estimation in the Age of Big Data
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Understanding Spatiotemporal Patterns of Biking Behavior by Analyzing Massive Bike Sharing Data in Chicago
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Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects
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Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
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Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
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Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction
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Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting
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Improving Short-Term Bike Sharing Demand Forecast Through an Irregular Convolutional Neural Network
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Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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