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STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-0-387-21763-5_11openalex-snapshot

Vector Autoregressive Models for Multivariate Time Series

inferredcites →https://doi.org/10.1016/j.trc.2019.01.027openalex-snapshot

DeepPF: A deep learning based architecture for metro passenger flow prediction

inferredcites →https://doi.org/10.1016/j.trc.2019.08.005openalex-snapshot

Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system

inferredcites →https://doi.org/10.1109/ijcnn.2018.8489234openalex-snapshot

ST-DRN: Deep Residual Networks for Spatio-Temporal Metro Stations Crowd Flows Forecast

inferredcites →https://doi.org/10.1109/tits.2017.2679179openalex-snapshot

Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data

inferredcites →https://doi.org/10.1109/tits.2018.2879497openalex-snapshot

Forecasting Short-Term Passenger Flow: An Empirical Study on Shenzhen Metro

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/tkde.2019.2891537openalex-snapshot

Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning

inferredcites →https://doi.org/10.1145/3085504.3085527openalex-snapshot

How the Passengers Flow in Complex Metro Networks?

inferredcites →https://doi.org/10.1145/3139958.3139984openalex-snapshot

City-wide Traffic Volume Inference with Loop Detector Data and Taxi Trajectories

inferredcites →https://doi.org/10.1145/3159652.3159682openalex-snapshot

Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks

inferredcites →https://doi.org/10.1145/3219819.3219931openalex-snapshot

StepDeep

inferredcites →https://doi.org/10.1145/3269206.3271757openalex-snapshot

Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization

inferredcites →https://doi.org/10.1145/3292500.3330877openalex-snapshot

Origin-Destination Matrix Prediction via Graph Convolution

inferredcites →https://doi.org/10.1145/3292500.3330884openalex-snapshot

Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning

inferredcites →https://doi.org/10.1145/3357384.3357870openalex-snapshot

DeepIST

inferredcites →https://doi.org/10.1145/3357384.3358097openalex-snapshot

Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction

inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot

Long Short-Term Memory

inferredcites →https://doi.org/10.1609/aaai.v31i1.10735openalex-snapshot

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

inferredcites →https://doi.org/10.1609/aaai.v32i1.11836openalex-snapshot

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

inferredcites →https://doi.org/10.1609/aaai.v33i01.33011020openalex-snapshot

DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis

inferredcites →https://doi.org/10.1609/aaai.v33i01.33013656openalex-snapshot

Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

inferredcites →https://doi.org/10.1609/aaai.v33i01.3301485openalex-snapshot

Gated Residual Recurrent Graph Neural Networks for Traffic Prediction

inferredcites →https://doi.org/10.1609/aaai.v33i01.3301890openalex-snapshot

Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting

inferredcites →https://doi.org/10.1609/aaai.v33i01.3301922openalex-snapshot

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

inferredcites →https://doi.org/10.1609/aaai.v34i04.5915openalex-snapshot

Tensor Completion for Weakly-Dependent Data on Graph for Metro Passenger Flow Prediction

inferredcites →https://doi.org/10.24963/ijcai.2018/519openalex-snapshot

Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns

inferredcites →https://doi.org/10.24963/ijcai.2019/264openalex-snapshot

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

inferredcites →https://doi.org/10.24963/ijcai.2019/274openalex-snapshot

STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

inferredcites →https://doi.org/10.24963/ijcai.2019/317openalex-snapshot

GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction

inferredcites →https://doi.org/10.3141/2024-14openalex-snapshot

Adaptive Seasonal Time Series Models for Forecasting Short-Term Traffic Flow

inferredcites →https://doi.org/10.48550/arxiv.1506.04214openalex-snapshot

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

inferredcites →https://doi.org/10.48550/arxiv.1707.01926openalex-snapshot

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

inferredcites →https://doi.org/10.48550/arxiv.1812.02019openalex-snapshot

Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4115556openalex-extracted

inferred← citeshttps://doi.org/10.1007/978-3-031-21127-0_43openalex-snapshot

Extracting Metro Passenger Flow Predictors from Network’s Complex Characteristics

inferred← citeshttps://doi.org/10.1007/978-981-97-2262-4_26openalex-snapshot

GSPM: An Early Detection Approach to Sudden Abnormal Large Outflow in a Metro System

inferred← citeshttps://doi.org/10.1007/s10707-022-00466-1openalex-snapshot

MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction

inferred← citeshttps://doi.org/10.1007/s11042-022-12077-xopenalex-snapshot

A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot prediction

inferred← citeshttps://doi.org/10.1007/s41019-024-00246-xopenalex-snapshot

An Overview Based on the Overall Architecture of Traffic Forecasting

inferred← citeshttps://doi.org/10.1007/s41109-023-00583-2openalex-snapshot

Exploring the association between network centralities and passenger flows in metro systems

inferred← citeshttps://doi.org/10.1016/j.eswa.2022.117921openalex-snapshot

Graph neural network for traffic forecasting: A survey

inferred← citeshttps://doi.org/10.1016/j.eswa.2022.118475openalex-snapshot

Forecasting network-wide multi-step metro ridership with an attention-weighted multi-view graph to sequence learning approach

inferred← citeshttps://doi.org/10.1016/j.eswa.2022.118790openalex-snapshot

Combining knowledge graph into metro passenger flow prediction: A split-attention relational graph convolutional network

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.120393openalex-snapshot

Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction

inferred← citeshttps://doi.org/10.1109/iaeac59436.2024.10503864openalex-snapshot

Dynamic Multi-Scale Spatio-Temporal Graph ODE for Metro Ridership Prediction

inferred← citeshttps://doi.org/10.1109/icde51399.2021.00321openalex-snapshot

BERT-based Dynamic Clustering of Subway Stations Using Flow Information

inferred← citeshttps://doi.org/10.1109/isie62713.2025.11124759openalex-snapshot

Metro Passenger Flow Prediction with GNN: Station Attribute Influence Analysis

inferred← citeshttps://doi.org/10.1109/tbdata.2022.3229836openalex-snapshot

Metro OD Matrix Prediction Based on Multi-View Passenger Flow Evolution Trend Modeling

inferred← citeshttps://doi.org/10.1109/tits.2022.3150600openalex-snapshot

Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow

inferred← citeshttps://doi.org/10.1109/tits.2024.3365179openalex-snapshot

Regularized Spatial–Temporal Graph Convolutional Networks for Metro Passenger Flow Prediction

inferred← citeshttps://doi.org/10.1109/tkde.2022.3187690openalex-snapshot

STP-TrellisNets+: Spatial-Temporal Parallel TrellisNets for Multi-Step Metro Station Passenger Flow Prediction

inferred← citeshttps://doi.org/10.1109/tkde.2023.3269771openalex-snapshot

Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction

inferred← citeshttps://doi.org/10.1117/12.3018521openalex-snapshot

Multilevel effects of factors influencing passenger flow in urban rail transit stations based on spatial metrology model

inferred← citeshttps://doi.org/10.1145/3488902openalex-snapshot

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

inferred← citeshttps://doi.org/10.1145/3579815openalex-snapshot

Spatio-temporal Graph Learning for Epidemic Prediction

inferred← citeshttps://doi.org/10.1145/3643822openalex-snapshot

Networked Time-series Prediction with Incomplete Data via Generative Adversarial Network

inferred← citeshttps://doi.org/10.1155/2023/8962283openalex-snapshot

BiLSTM‐ and GNN‐Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data

inferred← citeshttps://doi.org/10.1155/atr/7878081openalex-snapshot

Urban Traffic Flow Forecasting Based on Graph Structure Learning

inferred← citeshttps://doi.org/10.1631/fitee.2200621openalex-snapshot

A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

inferred← citeshttps://doi.org/10.3389/ffutr.2022.876880openalex-snapshot

Ensemble Models of For-Hire Vehicle Trips

inferred← citeshttps://doi.org/10.3390/electronics13101802openalex-snapshot

Ensemble Meta-Learning-Based Robust Chipping Prediction for Wafer Dicing

inferred← citeshttps://doi.org/10.3390/ijgi11020088openalex-snapshot

Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

inferred← citeshttps://doi.org/10.3390/iot2010003openalex-snapshot

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

inferred← citeshttps://doi.org/10.3390/math10244631openalex-snapshot

Detection and Prediction of Chipping in Wafer Grinding Based on Dicing Signal

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