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Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network

https://doi.org/10.1145/3292500.3330887
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1109/9780470544037.ch14openalex-snapshot
Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies
inferredcites →https://doi.org/10.1109/icassp.2013.6639343openalex-snapshot
Building high-level features using large scale unsupervised learning
inferredcites →https://doi.org/10.1109/icdm.2015.99openalex-snapshot
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inferredcites →https://doi.org/10.1109/tits.2013.2262376openalex-snapshot
Predicting Taxi–Passenger Demand Using Streaming Data
inferredcites →https://doi.org/10.1109/tits.2014.2345663openalex-snapshot
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inferredcites →https://doi.org/10.1126/science.1127647openalex-snapshot
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inferredcites →https://doi.org/10.1145/2783258.2783352openalex-snapshot
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inferredcites →https://doi.org/10.1145/2983323.2983667openalex-snapshot
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inferredcites →https://doi.org/10.1145/2996913.2997016openalex-snapshot
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inferredcites →https://doi.org/10.1145/3097983.3098018openalex-snapshot
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inferredcites →https://doi.org/10.1145/3159652.3159682openalex-snapshot
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inferredcites →https://doi.org/10.1145/3219819.3219822openalex-snapshot
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inferredcites →https://doi.org/10.1145/3219819.3220111openalex-snapshot
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inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot
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inferredcites →https://doi.org/10.1609/aaai.v31i1.10735openalex-snapshot
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inferredcites →https://doi.org/10.1609/aaai.v32i1.11836openalex-snapshot
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inferredcites →https://doi.org/10.24963/ijcai.2018/519openalex-snapshot
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inferredcites →https://doi.org/10.3390/s17071501openalex-snapshot
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inferred← citeshttps://doi.org/10.2139/ssrn.3986723openalex-extracted
inferred← citeshttps://doi.org/10.2139/ssrn.4053578openalex-extracted
Unsupervised Knowledge Adaptation for Passenger Demand Forecasting
inferred← citeshttps://doi.org/10.2139/ssrn.4687890openalex-extracted
Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network
inferred← citeshttps://doi.org/10.1002/cpe.70202openalex-snapshot
Explainable Graph Neural Networks for Optimized Allocation in Spatio‐Temporal Bike Sharing Demand Prediction
inferred← citeshttps://doi.org/10.1007/978-3-030-67670-4_27openalex-snapshot
Spatial Community-Informed Evolving Graphs for Demand Prediction
inferred← citeshttps://doi.org/10.1007/978-3-030-86130-8_21openalex-snapshot
ParkLSTM: Periodic Parking Behavior Prediction Based on LSTM with Multi-source Data for Contract Parking Spaces
inferred← citeshttps://doi.org/10.1007/978-3-030-86514-6_20openalex-snapshot
Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19
inferred← citeshttps://doi.org/10.1007/978-3-031-28813-5_2openalex-snapshot
On the Influence of Grid Cell Size on Taxi Demand Prediction
inferred← citeshttps://doi.org/10.1007/978-3-031-40292-0_10openalex-snapshot
Multivariate Long-Term Traffic Forecasting with Graph Convolutional Network and Historical Attention Mechanism
inferred← citeshttps://doi.org/10.1007/978-3-031-70344-7_5openalex-snapshot
Multivariate Traffic Demand Prediction via 2D Spectral Learning and Global Spatial Optimization
inferred← citeshttps://doi.org/10.1007/978-981-95-4381-6_23openalex-snapshot
A Seasonal-Trend Dynamic Interactive Integration Network for Traffic Forecasting
inferred← citeshttps://doi.org/10.1007/978-981-95-9354-5_16openalex-snapshot
Multimodal Traffic Flow Prediction Based on Causal Inference and Hidden Markov Chains
inferred← citeshttps://doi.org/10.1007/978-981-96-6954-7_27openalex-snapshot
Integrating Future Exogenous Information into Multi-mode Travel Demand Forecasting at Gateway Hubs
inferred← citeshttps://doi.org/10.1007/978-981-97-5498-4_18openalex-snapshot
Multi-mode Spatial-Temporal Data Modeling with Fully Connected Networks
inferred← citeshttps://doi.org/10.1007/s00521-022-07380-5openalex-snapshot
Bike sharing usage prediction with deep learning: a survey
inferred← citeshttps://doi.org/10.1007/s10489-020-02043-1openalex-snapshot
An effective dynamic spatiotemporal framework with external features information for traffic prediction
inferred← citeshttps://doi.org/10.1007/s10586-025-05136-zopenalex-snapshot
Apstgcn: adaptive parallel spatio-temporal graph convolution network for traffic forecasting
inferred← citeshttps://doi.org/10.1007/s11432-021-3487-xopenalex-snapshot
Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey
inferred← citeshttps://doi.org/10.1007/s12239-025-00233-9openalex-snapshot
RETRACTED ARTICLE: Spatio-temporal Dynamic Graph Convolutional Probability Sparse Attention Networks for Traffic Flow Forecasting
inferred← citeshttps://doi.org/10.1016/j.aei.2024.102533openalex-snapshot
Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network
inferred← citeshttps://doi.org/10.1016/j.array.2023.100283openalex-snapshot
Improved VIDAR and machine learning-based road obstacle detection method
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122766openalex-snapshot
Spatio-temporal Fourier enhanced heterogeneous graph learning for traffic forecasting
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.126108openalex-snapshot
Mixture of Spatial–Temporal Graph Transformer Networks for urban congestion prediction using multimodal transportation data
inferred← citeshttps://doi.org/10.1016/j.inffus.2023.101946openalex-snapshot
Generic Dynamic Graph Convolutional Network for traffic flow forecasting
inferred← citeshttps://doi.org/10.1016/j.ins.2023.119144openalex-snapshot
Short-term passenger flow prediction for multi-traffic modes: A Transformer and residual network based multi-task learning method
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.111946openalex-snapshot
Dynamic spatial aware graph transformer for spatiotemporal traffic flow forecasting
inferred← citeshttps://doi.org/10.1016/j.trc.2021.103352openalex-snapshot
A multi-task memory network with knowledge adaptation for multimodal demand forecasting
inferred← citeshttps://doi.org/10.1016/j.trc.2022.103731openalex-snapshot
Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach
inferred← citeshttps://doi.org/10.1016/j.trc.2022.103921openalex-snapshot
Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
inferred← citeshttps://doi.org/10.1016/j.trc.2022.103969openalex-snapshot
Copula ARMA-GARCH modelling of spatially and temporally correlated time series data for transportation planning use
inferred← citeshttps://doi.org/10.1016/j.trc.2023.104396openalex-snapshot
A non-local grouping tensor train decomposition model for travel demand analysis concerning categorical independent variables
inferred← citeshttps://doi.org/10.1016/j.trc.2023.104428openalex-snapshot
Network-wide short-term inflow prediction of the multi-traffic modes system: An adaptive multi-graph convolution and attention mechanism based multitask-learning model
inferred← citeshttps://doi.org/10.1016/j.trc.2025.105249openalex-snapshot
A spatial–Temporal Large Language Model with Denoising Diffusion Implicit for predictions in centralized multimodal transport systems
inferred← citeshttps://doi.org/10.1016/j.trc.2025.105257openalex-snapshot
Learning dynamic interaction for multimodal transportation systems: A joint passenger flow prediction approach based on spatiotemporal hypergraph convolution networks
inferred← citeshttps://doi.org/10.1061/(asce)is.1943-555x.0000705openalex-snapshot
Spatial Characterization of Strain Variation in the Profile of Tunnel Structure Using Monitoring Data and Numerical Modeling
inferred← citeshttps://doi.org/10.1080/23249935.2025.2499862openalex-snapshot
Knowledge adaptation with model sharing for passenger demand forecasting
inferred← citeshttps://doi.org/10.1109/access.2025.3579334openalex-snapshot
Frequency Enhanced Dynamic Graph Convolutional Networks for Traffic Flow Forecasting
inferred← citeshttps://doi.org/10.1109/aiotsys58602.2023.00053openalex-snapshot
Environment-agnostic Effective Learning for Domain Generalization on IoT Time Series Data
inferred← citeshttps://doi.org/10.1109/bdcat63179.2024.00045openalex-snapshot
Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020863openalex-snapshot
Graph Structure Neural Differential Equations on Spatio-temporal Prediction
inferred← citeshttps://doi.org/10.1109/bigdata59044.2023.10386527openalex-snapshot
M²–CNN: A Macro-Micro Model for Taxi Demand Prediction
inferred← citeshttps://doi.org/10.1109/bigdata59044.2023.10386990openalex-snapshot
Identifying Regions of High Demand for Transportation Services based on Cluster Evolution and Graph Analysis
inferred← citeshttps://doi.org/10.1109/cec60901.2024.10612119openalex-snapshot
Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting
inferred← citeshttps://doi.org/10.1109/dsc50466.2020.00009openalex-snapshot
GeST: A Grid Embedding based Spatio-Temporal Correlation Model for Crime Prediction
inferred← citeshttps://doi.org/10.1109/icaase56196.2022.9931585openalex-snapshot
The Impact of Big Data Analytics on Traffic Prediction
inferred← citeshttps://doi.org/10.1109/icecai58670.2023.10176751openalex-snapshot
Shared Bike Demand Prediction Based on Combined Deep Learnings
inferred← citeshttps://doi.org/10.1109/iciba62489.2024.10869213openalex-snapshot
Spatio-Temporal Multi-Embedded Attention Aware Transformer for Traffic Flow Prediction
inferred← citeshttps://doi.org/10.1109/ijcnn60899.2024.10650903openalex-snapshot
Spatio-Temporal Dynamically Fused Graph Convolutional Network
inferred← citeshttps://doi.org/10.1109/itsc55140.2022.9922276openalex-snapshot
Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approach
inferred← citeshttps://doi.org/10.1109/itsc57777.2023.10421851openalex-snapshot
TEDGCN: Asymmetric Spatiotemporal GNN for Heterogeneous Traffic Prediction
inferred← citeshttps://doi.org/10.1109/itsc57777.2023.10422357openalex-snapshot
Demand Density Forecasting in Mobility-on-Demand Systems Through Recurrent Mixture Density Networks
inferred← citeshttps://doi.org/10.1109/jiot.2021.3115174openalex-snapshot
Learning Shared Mobility-Aware Knowledge for Multiple Urban Travel Demands
inferred← citeshttps://doi.org/10.1109/jiot.2022.3185010openalex-snapshot
CoupledMUTS: Coupled Multivariate Utility Time-Series Representation and Prediction
inferred← citeshttps://doi.org/10.1109/jiot.2024.3442922openalex-snapshot
ShieldTSE : A Privacy-Enhanced Split Federated Learning Framework for Traffic State Estimation in IoV
inferred← citeshttps://doi.org/10.1109/jsen.2021.3115405openalex-snapshot
Classification of fNIRS Finger Tapping Data With Multi-Labeling and Deep Learning
inferred← citeshttps://doi.org/10.1109/mdm61037.2024.00043openalex-snapshot
xMTrans: Temporal Attentive Cross-Modality Fusion Transformer for Long-Term Traffic Prediction
inferred← citeshttps://doi.org/10.1109/mt-its68460.2025.11223524openalex-snapshot
Learning Traffic Flows: Graph Neural Networks for Metamodelling Traffic Assignment
inferred← citeshttps://doi.org/10.1109/swc57546.2023.10448727openalex-snapshot
Graph Convolutional Network with Gated Multi-mode Fusion for Traffic Forecasting
inferred← citeshttps://doi.org/10.1109/tbdata.2020.3047078openalex-snapshot
Predicting Fine-Grained Air Quality Based on Deep Neural Networks
inferred← citeshttps://doi.org/10.1109/tbdata.2024.3414280openalex-snapshot
Cross-Modality and Equity-Aware Graph Pooling Fusion: A Bike Mobility Prediction Study
inferred← citeshttps://doi.org/10.1109/tcss.2025.3626644openalex-snapshot
Dynamic Graph Convolutional Network With Spatiotemporal and Multiscale Feature Fusion for Multistep Traffic Flow Prediction
inferred← citeshttps://doi.org/10.1109/tcyb.2021.3117705openalex-snapshot
Understanding Private Car Aggregation Effect via Spatio-Temporal Analysis of Trajectory Data
inferred← citeshttps://doi.org/10.1109/tetci.2024.3352407openalex-snapshot
MDG: A Multi-Task Dynamic Graph Generation Framework for Multivariate Time Series Forecasting
inferred← citeshttps://doi.org/10.1109/tifs.2025.3621008openalex-snapshot
Dual Dependency Disentangling for Defending Model Inversion Attacks in Split Federated Learning
inferred← citeshttps://doi.org/10.1109/tits.2020.2966498openalex-snapshot
Traffic Demand Prediction Based on Dynamic Transition Convolutional Neural Network
inferred← citeshttps://doi.org/10.1109/tits.2020.3015542openalex-snapshot
Taxi Demand Prediction Using Parallel Multi-Task Learning Model
inferred← citeshttps://doi.org/10.1109/tits.2021.3054840openalex-snapshot
Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions
inferred← citeshttps://doi.org/10.1109/tits.2021.3055207openalex-snapshot
Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks
inferred← citeshttps://doi.org/10.1109/tits.2021.3056415openalex-snapshot
A Multi-Task Matrix Factorized Graph Neural Network for Co-Prediction of Zone-Based and OD-Based Ride-Hailing Demand
inferred← citeshttps://doi.org/10.1109/tits.2021.3080511openalex-snapshot
MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis
inferred← citeshttps://doi.org/10.1109/tits.2021.3119887openalex-snapshot
Multi-Semantic Path Representation Learning for Travel Time Estimation
inferred← citeshttps://doi.org/10.1109/tits.2022.3155753openalex-snapshot
Adaptive Spatiotemporal Dependence Learning for Multi-Mode Transportation Demand Prediction
inferred← citeshttps://doi.org/10.1109/tits.2023.3234444openalex-snapshot
Swarm Learning-Based Dynamic Optimal Management for Traffic Congestion in 6G-Driven Intelligent Transportation System
inferred← citeshttps://doi.org/10.1109/tits.2023.3239101openalex-snapshot
Adaptive Feature Fusion Networks for Origin-Destination Passenger Flow Prediction in Metro Systems
inferred← citeshttps://doi.org/10.1109/tits.2023.3243913openalex-snapshot
Spatio-Temporal AutoEncoder for Traffic Flow Prediction
inferred← citeshttps://doi.org/10.1109/tits.2023.3312224openalex-snapshot
Coupling Makes Better: An Intertwined Neural Network for Taxi and Ridesourcing Demand Co-Prediction
inferred← citeshttps://doi.org/10.1109/tits.2023.3322717openalex-snapshot
Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction Using Domain-Adversarial Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/tits.2023.3323945openalex-snapshot
Continuous-Time and Discrete-Time Representation Learning for Origin-Destination Demand Prediction
inferred← citeshttps://doi.org/10.1109/tits.2023.3325936openalex-snapshot
Multimodal Transport Demand Forecasting via Federated Learning
inferred← citeshttps://doi.org/10.1109/tits.2025.3614270openalex-snapshot
Leveraging Spatial–Temporal Heterogeneity and Cross-Mode Interactions: A Meta-Learning Approach for Multimodal Transportation Demand Prediction
inferred← citeshttps://doi.org/10.1109/tits.2025.3639619openalex-snapshot
Multi-Mode Spatiotemporal Adaptive Fusion Network for Travel Demand Prediction
inferred← citeshttps://doi.org/10.1109/tits.2026.3685287openalex-snapshot
Multi-Scale Spatiotemporal-Enhanced Network for Multimodal Traffic Flow Joint Prediction
inferred← citeshttps://doi.org/10.1109/tits.2026.3704750openalex-snapshot
EVCha: A Multi-Task Learning Framework With Heterogeneous Multi-Graph Fusion for Electric Vehicle Charging Demand Prediction
inferred← citeshttps://doi.org/10.1109/tkde.2019.2956713openalex-snapshot
Spatio-Temporal Multi-Task Learning via Tensor Decomposition
inferred← citeshttps://doi.org/10.1109/tkde.2020.3025580openalex-snapshot
Deep Learning for Spatio-Temporal Data Mining: A Survey
inferred← citeshttps://doi.org/10.1109/tkde.2021.3077056openalex-snapshot
DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
inferred← citeshttps://doi.org/10.1109/tkde.2022.3180726openalex-snapshot
Classification-Labeled Continuousization and Multi-Domain Spatio-Temporal Fusion for Fine-Grained Urban Crime Prediction
inferred← citeshttps://doi.org/10.1109/tkde.2024.3383663openalex-snapshot
Bootstrapping on Continuous-Time Dynamic Graphs for Crowd Flow Modeling
inferred← citeshttps://doi.org/10.1109/tmc.2021.3119421openalex-snapshot
Optimizing Cross-Line Dispatching for Minimum Electric Bus Fleet
inferred← citeshttps://doi.org/10.1109/tmc.2023.3296501openalex-snapshot
Predicting Collective Human Mobility via Countering Spatiotemporal Heterogeneity
inferred← citeshttps://doi.org/10.1109/tmc.2023.3310789openalex-snapshot
Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1109/tmc.2024.3517332openalex-snapshot
Exploring Context Generalizability in Citywide Crowd Mobility Prediction: An Analytic Framework and Benchmark
inferred← citeshttps://doi.org/10.1109/tpami.2025.3576805openalex-snapshot
ComS2T: A Complementary Spatiotemporal Learning System for Data-Adaptive Model Evolution
inferred← citeshttps://doi.org/10.1109/tpami.2025.3627280openalex-snapshot
Joint Short-Term Origin-Destination Demand Prediction for Multimodal Transport Systems
inferred← citeshttps://doi.org/10.1109/tvt.2024.3397716openalex-snapshot
Condition-Guided Urban Traffic Co-Prediction With Multiple Sparse Surveillance Data
inferred← citeshttps://doi.org/10.1111/tgis.70323openalex-snapshot
A Multi‐View Spatio‐Temporal Learning Model for the Co‐Prediction of Multimodal Origin–Destination Travel Demand
inferred← citeshttps://doi.org/10.1145/3340531.3411920openalex-snapshot
Deep Spatio-Temporal Multiple Domain Fusion Network for Urban Anomalies Detection
inferred← citeshttps://doi.org/10.1145/3340531.3411965openalex-snapshot
Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network
inferred← citeshttps://doi.org/10.1145/3442381.3449817openalex-snapshot
STUaNet: Understanding Uncertainty in Spatiotemporal Collective Human Mobility
inferred← citeshttps://doi.org/10.1145/3447548.3467062openalex-snapshot
Improving the Information Disclosure in Mobility-on-Demand Systems
inferred← citeshttps://doi.org/10.1145/3447548.3467275openalex-snapshot
Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting
inferred← citeshttps://doi.org/10.1145/3450528openalex-snapshot
The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting
inferred← citeshttps://doi.org/10.1145/3459637.3482482openalex-snapshot
Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
inferred← citeshttps://doi.org/10.1145/3472300openalex-snapshot
Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System
inferred← citeshttps://doi.org/10.1145/3488560.3498394openalex-snapshot
CMT-Net: A Mutual Transition Aware Framework for Taxicab Pick-ups and Drop-offs Co-Prediction
inferred← citeshttps://doi.org/10.1145/3526087openalex-snapshot
DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction
inferred← citeshttps://doi.org/10.1145/3529091openalex-snapshot
Predicting a Person’s Next Activity Region with a Dynamic Region-Relation-Aware Graph Neural Network
inferred← citeshttps://doi.org/10.1145/3534678.3539060openalex-snapshot
Optimizing Long-Term Efficiency and Fairness in Ride-Hailing via Joint Order Dispatching and Driver Repositioning
inferred← citeshttps://doi.org/10.1145/3534678.3539078openalex-snapshot
Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams
inferred← citeshttps://doi.org/10.1145/3534678.3539274openalex-snapshot
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
inferred← citeshttps://doi.org/10.1145/3534678.3539397openalex-snapshot
MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting
inferred← citeshttps://doi.org/10.1145/3536427openalex-snapshot
CausalSE: Understanding Varied Spatial Effects with Missing Data Towards Adding New Bike-sharing Stations
inferred← citeshttps://doi.org/10.1145/3565578openalex-snapshot
STHAN: Transportation Demand Forecasting with Compound Spatio-Temporal Relationships
inferred← citeshttps://doi.org/10.1145/3579815openalex-snapshot
Spatio-temporal Graph Learning for Epidemic Prediction
inferred← citeshttps://doi.org/10.1145/3580305.3599463openalex-snapshot
Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction
inferred← citeshttps://doi.org/10.1145/3580305.3599492openalex-snapshot
Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training
inferred← citeshttps://doi.org/10.1145/3580305.3599760openalex-snapshot
A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management
inferred← citeshttps://doi.org/10.1145/3605894openalex-snapshot
TTS-Norm: Forecasting Tensor Time Series via Multi-Way Normalization
inferred← citeshttps://doi.org/10.1145/3616855.3635759openalex-snapshot
CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting
inferred← citeshttps://doi.org/10.1145/3637528.3672030openalex-snapshot
MulSTE: A Multi-view Spatio-temporal Learning Framework with Heterogeneous Event Fusion for Demand-supply Prediction
inferred← citeshttps://doi.org/10.1145/3652859openalex-snapshot
DeepMeshCity: A Deep Learning Model for Urban Grid Prediction
inferred← citeshttps://doi.org/10.1145/3746709.3746760openalex-snapshot
ASTGCL: Adaptive Spatio-Temporal Graph-enhanced Contrastive Learning for Traffic Flow Prediction
inferred← citeshttps://doi.org/10.1145/3748636.3762709openalex-snapshot
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
inferred← citeshttps://doi.org/10.1145/3770855.3818022openalex-snapshot
Frequency-Domain Multi-Modality Transportation Modeling
inferred← citeshttps://doi.org/10.1145/3776552openalex-snapshot
JiuTian·Chuanliu: A Large Spatiotemporal Model for General-purpose Dynamic Urban Sensing
inferred← citeshttps://doi.org/10.1145/3810158.3810168openalex-snapshot
Empower Online Education with Continuous-Time Dynamic Graph Representation Learning
inferred← citeshttps://doi.org/10.1155/2021/4727617openalex-snapshot
Research on Community Public Service Information Collaborative Governance Based on Deep Learning Model
inferred← citeshttps://doi.org/10.1155/2022/8712597openalex-snapshot
A Privacy-Preserving Crowd Flow Prediction Framework Based on Federated Learning during Epidemics
inferred← citeshttps://doi.org/10.1177/03611981241308868openalex-snapshot
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inferred← citeshttps://doi.org/10.1177/14759217211044803openalex-snapshot
Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data
inferred← citeshttps://doi.org/10.1371/journal.pone.0323787openalex-snapshot
T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks
inferred← citeshttps://doi.org/10.14778/3734839.3734862openalex-snapshot
GraphSparseNet: A Novel Method for Large Scale Traffic Flow Prediction
inferred← citeshttps://doi.org/10.1609/aaai.v35i1.16107openalex-snapshot
Community-Aware Multi-Task Transportation Demand Prediction
inferred← citeshttps://doi.org/10.1609/aaai.v35i5.16591openalex-snapshot
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
inferred← citeshttps://doi.org/10.1609/aaai.v35i5.16603openalex-snapshot
Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction
inferred← citeshttps://doi.org/10.1609/aaai.v36i4.20342openalex-snapshot
Event-Aware Multimodal Mobility Nowcasting
inferred← citeshttps://doi.org/10.1609/aaai.v37i12.26675openalex-snapshot
Point-to-Region Co-learning for Poverty Mapping at High Resolution Using Satellite Imagery
inferred← citeshttps://doi.org/10.1609/aaai.v37i4.25619openalex-snapshot
Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction
inferred← citeshttps://doi.org/10.1609/aaai.v38i8.28707openalex-snapshot
Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting
inferred← citeshttps://doi.org/10.17148/ijarcce.2024.13223openalex-snapshot
Predicting Air Quality by Particulate Matter Based on Neural Networks
inferred← citeshttps://doi.org/10.2139/ssrn.4860917openalex-snapshot
Predicting Short-Term Bike-Sharing Demand at Station Level: A Deep Graph-Based Multi-Task Spatiotemporal Approach
inferred← citeshttps://doi.org/10.2139/ssrn.4903667openalex-snapshot
Predicting Short-Term Bike-Sharing Demand at Station Level: A Semi-Adaptive Graph-Based Multi-Task Spatiotemporal Approach
inferred← citeshttps://doi.org/10.22214/ijraset.2022.41879openalex-snapshot
A Survey on Time Series Forecasting Approaches and Applications
inferred← citeshttps://doi.org/10.22214/ijraset.2022.42386openalex-snapshot
Taxi Demand Forecasting Utilizing Neighbourhood Influence Based on Ensemble Learning
inferred← citeshttps://doi.org/10.24963/ijcai.2022/282openalex-snapshot
When Transfer Learning Meets Cross-City Urban Flow Prediction: Spatio-Temporal Adaptation Matters
inferred← citeshttps://doi.org/10.24963/ijcai.2023/231openalex-snapshot
Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
inferred← citeshttps://doi.org/10.3233/faia200310openalex-snapshot
ST-MFM: A Spatiotemporal Multi-Modal Fusion Model for Urban Anomalies Prediction
inferred← citeshttps://doi.org/10.3233/faia240813openalex-snapshot
Spatially Constrained Transformer with Efficient Global Relation Modelling for Spatio-Temporal Prediction
inferred← citeshttps://doi.org/10.3390/logistics7040086openalex-snapshot
Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers
inferred← citeshttps://doi.org/10.3390/s23020594openalex-snapshot
A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand
inferred← citeshttps://doi.org/10.4218/etrij.2021-0123openalex-snapshot
Taxi‐demand forecasting using dynamic spatiotemporal analysis
inferred← citeshttps://doi.org/10.48550/arxiv.2005.05128openalex-snapshot
An Effective Dynamic Spatio-temporal Framework with Multi-Source Information for Traffic Prediction
inferred← citeshttps://doi.org/10.48550/arxiv.2111.14625openalex-snapshot
Cyclic Graph Attentive Match Encoder (CGAME): A Novel Neural Network For OD Estimation
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