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Prediction of Public Bus Passenger Flow Using Spatial–Temporal Hybrid Model of Deep Learning

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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/s00521-021-05966-zopenalex-snapshot

Dual attentive graph neural network for metro passenger flow prediction

inferredcites →https://doi.org/10.1007/s11042-020-09088-xopenalex-snapshot

Study on Subway passenger flow prediction based on deep recurrent neural network

inferredcites →https://doi.org/10.1016/j.asoc.2017.05.011openalex-snapshot

A multi-pattern deep fusion model for short-term bus passenger flow forecasting

inferredcites →https://doi.org/10.1016/j.eswa.2018.12.031openalex-snapshot

Daily long-term traffic flow forecasting based on a deep neural network

inferredcites →https://doi.org/10.1016/j.knosys.2017.02.016openalex-snapshot

Passenger flow estimation based on convolutional neural network in public transportation system

inferredcites →https://doi.org/10.1016/j.trc.2015.03.014openalex-snapshot

Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

inferredcites →https://doi.org/10.1016/j.trc.2017.02.005openalex-snapshot

Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks

inferredcites →https://doi.org/10.1016/j.trc.2017.10.016openalex-snapshot

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.2020.102620openalex-snapshot

Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction

inferredcites →https://doi.org/10.1061/9780784482292.242openalex-snapshot

Short-Term Forecasting of Passenger Flow on the Metro Platform Using an Improved Kalman Filtering Method

inferredcites →https://doi.org/10.1061/jhtrcq.0000255openalex-snapshot

Highway Traffic Volume Forecasting Based on Seasonal ARIMA Model

inferredcites →https://doi.org/10.1109/cbd51900.2020.00030openalex-snapshot

Traffic Flow Prediction via Spatial Temporal Neural Network "ResLS-C"

inferredcites →https://doi.org/10.1109/icpads51040.2020.00095openalex-snapshot

Dynamic-Static-based Spatiotemporal Multi-Graph Neural Networks for Passenger Flow Prediction

inferredcites →https://doi.org/10.1109/tits.2019.2935152openalex-snapshot

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

inferredcites →https://doi.org/10.1109/uv50937.2020.9426223openalex-snapshot

Attention-based Graph Neural Network Enabled Method to Predict Short-term Metro Passenger Flow

inferredcites →https://doi.org/10.1155/2020/4656435openalex-snapshot

A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction

inferredcites →https://doi.org/10.1524/auto.2001.49.11.505openalex-snapshot

Traffic Flow Forecasting

inferredcites →https://doi.org/10.1609/aaai.v33i01.33015668openalex-snapshot

Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

inferredcites →https://doi.org/10.1609/aaai.v34i01.5477openalex-snapshot

GMAN: A Graph Multi-Attention Network for Traffic Prediction

inferredcites →https://doi.org/10.24963/ijcai.2018/476openalex-snapshot

GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction

inferredcites →https://doi.org/10.24963/ijcai.2018/505openalex-snapshot

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

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/10.3390/ijgi8060243openalex-snapshot

Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.1409.0473openalex-snapshot

Neural Machine Translation by Jointly Learning to Align and Translate

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

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

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.5013948openalex-extracted

Gravattn: A Spatially Transferable Gravity Model for Trip Distribution

inferred← citeshttps://doi.org/10.1007/978-981-99-6090-3_34openalex-snapshot

Spatio-Temporal Factors Affecting Short-Term Public Transit Passenger Demand Prediction: A Review

inferred← citeshttps://doi.org/10.1007/s12469-024-00365-8openalex-snapshot

Deep learning-based public transit passenger flow prediction model: integration of weather and temporal attributes

inferred← citeshttps://doi.org/10.1016/j.cstp.2024.101230openalex-snapshot

Origin-destination demand prediction of public transit using graph convolutional neural network

inferred← citeshttps://doi.org/10.1016/j.omega.2024.103105openalex-snapshot

Leveraging explainable artificial intelligence in understanding public transportation usage rates for sustainable development

inferred← citeshttps://doi.org/10.1016/j.trc.2025.105107openalex-snapshot

TMS-GNN: Traffic-aware Multistep Graph Neural Network for bus passenger flow prediction

inferred← citeshttps://doi.org/10.1016/j.tre.2025.104011openalex-snapshot

Arrival information-guided spatiotemporal prediction of transportation hub passenger distribution

inferred← citeshttps://doi.org/10.1061/jtepbs.teeng-8794openalex-snapshot

Predicting Regional-Level Bus Stop Passenger Flow with a Multigraph Fusion Spatio-Temporal Graph Attention Network

inferred← citeshttps://doi.org/10.1061/jtepbs.teeng-8830openalex-snapshot

Passenger Flow Prediction Based on a Hybrid Method in the Nanjing Metro System

inferred← citeshttps://doi.org/10.1061/jtepbs.teeng-8981openalex-snapshot

Urban Rail Transit Passenger Flow Prediction with Gaussian Process Regression Based on CEEMDAN

inferred← citeshttps://doi.org/10.1109/icsc67292.2026.00021openalex-snapshot

An Explainable GNN-Based Framework for Short-Term Bus Passenger Flow Forecasting

inferred← citeshttps://doi.org/10.1109/itsc57777.2023.10422353openalex-snapshot

Multi-STGAC: A Graph Attention Based Model for Short-term Bus Passenger Flow Forecasting

inferred← citeshttps://doi.org/10.1109/jiot.2024.3452422openalex-snapshot

A Macroscopic-Fundamental-Function-Aided Neural Networks for Traffic Flows Prediction From Mobile Signaling Data

inferred← citeshttps://doi.org/10.1109/tits.2025.3632753openalex-snapshot

A Learning Energy Management System for Fuel Cell Electric Buses Considering Passenger Flow Prediction and Speed Planning

inferred← citeshttps://doi.org/10.1109/tits.2026.3652490openalex-snapshot

STAM-GED: Spatial–Temporal Attention Masked Graph Neural Networks for Passenger Flow Prediction

inferred← citeshttps://doi.org/10.26518/2071-7296-2025-22-6-976-985openalex-snapshot

Forecasting of public bus passenger flow in the Voronezh region

inferred← citeshttps://doi.org/10.3390/app13084910openalex-snapshot

Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network

inferred← citeshttps://doi.org/10.3390/math10091428openalex-snapshot

Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain

inferred← citeshttps://doi.org/10.3390/su142114297openalex-snapshot

Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

inferred← citeshttps://doi.org/10.3390/su152014889openalex-snapshot

Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM

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