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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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inferredcites →https://doi.org/10.1007/bf00994018openalex-extracted
Support-Vector Networks
inferredcites →https://doi.org/10.1007/s10618-023-00926-8openalex-extracted
Parameterizing the cost function of dynamic time warping with application to time series classification
inferredcites →https://doi.org/10.1007/s11704-011-1192-6openalex-extracted
Prediction of urban human mobility using large-scale taxi traces and its applications
inferredcites →https://doi.org/10.1016/j.compenvurbsys.2018.05.009openalex-extracted
Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity
inferredcites →https://doi.org/10.1016/j.compenvurbsys.2019.02.002openalex-extracted
Unravel the landscape and pulses of cycling activities from a dockless bike-sharing system
inferredcites →https://doi.org/10.1016/j.inffus.2022.11.019openalex-extracted
Hybrid deep learning models for traffic prediction in large-scale road networks
inferredcites →https://doi.org/10.1016/j.inffus.2023.02.033openalex-extracted
The relative roles of different land-use types in bike-sharing demand: A machine learning-based multiple interpolation fusion method
inferredcites →https://doi.org/10.1016/j.inffus.2023.102178openalex-extracted
Optimization of spatial-temporal graph: A taxi demand forecasting model based on spatial-temporal tree
inferredcites →https://doi.org/10.1016/j.inffus.2024.102293openalex-extracted
Spatiotemporal Fusion Transformer for large-scale traffic forecasting
inferredcites →https://doi.org/10.1016/j.inffus.2024.102294openalex-extracted
Time-dependent trip generation for bike sharing planning: A multi-task memory-augmented graph neural network
inferredcites →https://doi.org/10.1016/j.inffus.2024.102339openalex-extracted
Temporal-aware structure-semantic-coupled graph network for traffic forecasting
inferredcites →https://doi.org/10.1016/j.inffus.2024.102466openalex-extracted
Enhancement of traffic forecasting through graph neural network-based information fusion techniques
inferredcites →https://doi.org/10.1016/j.ipm.2023.103387openalex-extracted
Dual-norm based dynamic graph diffusion network for temporal prediction
inferredcites →https://doi.org/10.1016/j.jtrangeo.2016.07.003openalex-extracted
Bike-share rebalancing strategies, patterns, and purpose
inferredcites →https://doi.org/10.1016/j.jtte.2019.07.002openalex-extracted
A multi-task deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences
inferredcites →https://doi.org/10.1016/j.neucom.2018.10.097openalex-extracted
A deep learning based multitask model for network-wide traffic speed prediction
inferredcites →https://doi.org/10.1016/j.pmcj.2010.07.002openalex-extracted
Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system
inferredcites →https://doi.org/10.1016/j.tra.2015.03.017openalex-extracted
A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand
inferredcites →https://doi.org/10.1016/j.tra.2021.09.010openalex-extracted
Relocating operational and damaged bikes in free-floating systems: A data-driven modeling framework for level of service enhancement
inferredcites →https://doi.org/10.1016/j.trb.2018.01.007openalex-extracted
Exact loading and unloading strategies for the static multi-vehicle bike repositioning problem
inferredcites →https://doi.org/10.1016/j.trc.2017.10.010openalex-extracted
Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns
inferredcites →https://doi.org/10.1016/j.trc.2017.10.016openalex-extracted
Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
inferredcites →https://doi.org/10.1016/j.trc.2018.10.011openalex-extracted
Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
inferredcites →https://doi.org/10.1016/j.trc.2019.04.006openalex-extracted
A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system
inferredcites →https://doi.org/10.1016/j.trc.2020.102858openalex-extracted
Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network
inferredcites →https://doi.org/10.1016/j.trc.2021.103063openalex-extracted
Joint predictions of multi-modal ride-hailing demands: A deep multi-task multi-graph learning-based approach
inferredcites →https://doi.org/10.1016/j.trc.2022.103731openalex-extracted
Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach
inferredcites →https://doi.org/10.1016/j.trc.2022.103984openalex-extracted
Improving short-term bike sharing demand forecast through an irregular convolutional neural network
inferredcites →https://doi.org/10.1016/j.tre.2014.10.005openalex-extracted
Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem
inferredcites →https://doi.org/10.1016/j.trpro.2018.11.029openalex-extracted
Short-term prediction for bike-sharing service using machine learning
inferredcites →https://doi.org/10.1049/iet-its.2018.0064openalex-extracted
Deep learning methods in transportation domain: a review
inferredcites →https://doi.org/10.1049/iet-its.2020.0305openalex-extracted
Forecasting usage and bike distribution of dockless bike‐sharing using journey data
inferredcites →https://doi.org/10.1061/(asce)0733-947x(1995)121:3(249)openalex-extracted
Short-Term Prediction of Traffic Volume in Urban Arterials
inferredcites →https://doi.org/10.1080/13658816.2019.1652303openalex-extracted
A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes
inferredcites →https://doi.org/10.1080/15568318.2019.1611976openalex-extracted
Dynamic linear models to predict bike availability in a bike sharing system
inferredcites →https://doi.org/10.1109/access.2020.2987934openalex-extracted
A Deep Learning Based Multi-Block Hybrid Model for Bike-Sharing Supply-Demand Prediction
inferredcites →https://doi.org/10.1109/cyberc.2019.00081openalex-extracted
Passenger Flow Prediction in Bus Transportation System using ARIMA Models with Big Data
inferredcites →https://doi.org/10.1109/icdm.2016.0061openalex-extracted
Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method
inferredcites →https://doi.org/10.1109/itsc.2018.8569427openalex-extracted
Travel Demand Prediction using Deep Multi-Scale Convolutional LSTM Network
inferredcites →https://doi.org/10.1109/mdm.2012.16openalex-extracted
Cityride: A Predictive Bike Sharing Journey Advisor
inferredcites →https://doi.org/10.1109/mits.2021.3049362openalex-extracted
Understanding and Predicting the Short-Term Passenger Flow of Station-Free Shared Bikes: A Spatiotemporal Deep Learning Approach
inferredcites →https://doi.org/10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00153openalex-extracted
BikeNet: Accurate Bike Demand Prediction Using Graph Neural Networks for Station Rebalancing
inferredcites →https://doi.org/10.1109/tits.2004.837813openalex-extracted
Travel-Time Prediction With Support Vector Regression
inferredcites →https://doi.org/10.1109/tits.2017.2755684openalex-extracted
Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks
inferredcites →https://doi.org/10.1109/tits.2018.2886456openalex-extracted
Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems
inferredcites →https://doi.org/10.1109/tits.2019.2900481openalex-extracted
Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction
inferredcites →https://doi.org/10.1109/tits.2019.2909571openalex-extracted
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-extracted
Taxi Demand Prediction Using Parallel Multi-Task Learning Model
inferredcites →https://doi.org/10.1109/tits.2021.3080511openalex-extracted
MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis
inferredcites →https://doi.org/10.1109/tits.2021.3097240openalex-extracted
Short-Term Forecast of Bicycle Usage in Bike Sharing Systems: A Spatial-Temporal Memory Network
inferredcites →https://doi.org/10.1109/tkde.2019.2898831openalex-extracted
Citywide Bike Usage Prediction in a Bike-Sharing System
inferredcites →https://doi.org/10.1109/tkde.2019.2948005openalex-extracted
Multi-level Attention Networks for Multi-step Citywide Passenger Demands Prediction
inferredcites →https://doi.org/10.1109/tkde.2021.3070203openalex-extracted
A Survey on Multi-Task Learning
inferredcites →https://doi.org/10.1109/vtcspring.2017.8108575openalex-extracted
Prediction of Station Level Demand in a Bike Sharing System Using Recurrent Neural Networks
inferredcites →https://doi.org/10.1109/yac.2016.7804912openalex-extracted
Using LSTM and GRU neural network methods for traffic flow prediction
inferredcites →https://doi.org/10.1145/2820783.2820837openalex-extracted
Traffic prediction in a bike-sharing system
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-extracted
XGBoost
inferredcites →https://doi.org/10.1145/2971648.2971652openalex-extracted
Dynamic cluster-based over-demand prediction in bike sharing systems
inferredcites →https://doi.org/10.1145/3219819.3219873openalex-extracted
Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems
inferredcites →https://doi.org/10.1145/3274895.3274896openalex-extracted
Bike flow prediction with multi-graph convolutional networks
inferredcites →https://doi.org/10.1145/3292500.3330887openalex-extracted
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-extracted
Long Short-Term Memory
inferredcites →https://doi.org/10.1177/0361198118801354openalex-extracted
Use of Deep Learning to Predict Daily Usage of Bike Sharing Systems
inferredcites →https://doi.org/10.1287/opre.2018.1784openalex-extracted
Travel Time Estimation in the Age of Big Data
inferredcites →https://doi.org/10.1371/journal.pone.0137922openalex-extracted
Understanding Spatiotemporal Patterns of Biking Behavior by Analyzing Massive Bike Sharing Data in Chicago
inferredcites →https://doi.org/10.1371/journal.pone.0220782openalex-extracted
Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects
inferredcites →https://doi.org/10.1609/aaai.v31i1.10735openalex-extracted
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
inferredcites →https://doi.org/10.1609/aaai.v32i1.11836openalex-extracted
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
inferredcites →https://doi.org/10.1609/aaai.v33i01.33011004openalex-extracted
Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction
inferredcites →https://doi.org/10.1609/aaai.v33i01.33013656openalex-extracted
Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting
inferredcites →https://doi.org/10.2139/ssrn.4032616openalex-extracted
Improving Short-Term Bike Sharing Demand Forecast Through an Irregular Convolutional Neural Network
inferredcites →https://doi.org/10.2307/143141openalex-extracted
A Computer Movie Simulating Urban Growth in the Detroit Region
inferredcites →https://doi.org/10.3390/rs11111265openalex-extracted
Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning
inferredcites →https://doi.org/10.48550/arxiv.1912.01703openalex-extracted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
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