On the public citation graph

https://doi.org/10.2139/ssrn.5078714

0 signed · 43 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1002/ett.4427openalex-extracted
Machine learning for next‐generation intelligent transportation systems: A survey
inferredcites →https://doi.org/10.1002/widm.1457openalex-extracted
A review of bus arrival time prediction using artificial intelligence
inferredcites →https://doi.org/10.1007/s11116-023-10385-1openalex-extracted
Comparative analysis of deep-learning-based models for hourly bus passenger flow forecasting
inferredcites →https://doi.org/10.1007/s11831-022-09740-9openalex-extracted
Eighty Years of the Finite Element Method: Birth, Evolution, and Future
inferredcites →https://doi.org/10.1007/s12469-022-00301-8openalex-extracted
Robustness and disturbances in public transport
inferredcites →https://doi.org/10.1016/j.engappai.2024.109166openalex-extracted
Machine Learning for public transportation demand prediction: A Systematic Literature Review
inferredcites →https://doi.org/10.1016/j.eswa.2021.116057openalex-extracted
Forecasting the subway passenger flow under event occurrences with multivariate disturbances
inferredcites →https://doi.org/10.1016/j.inffus.2024.102592openalex-extracted
TDF-Net: Trusted Dynamic Feature Fusion Network for breast cancer diagnosis using incomplete multimodal ultrasound
inferredcites →https://doi.org/10.1016/j.ins.2021.02.036openalex-extracted
A novel prediction model for the inbound passenger flow of urban rail transit
inferredcites →https://doi.org/10.1016/j.ins.2021.08.065openalex-extracted
A new prediction strategy for dynamic multi-objective optimization using Gaussian Mixture Model
inferredcites →https://doi.org/10.1016/j.ins.2024.121109openalex-extracted
Short-term subway passenger flow forecasting approach based on multi-source data fusion
inferredcites →https://doi.org/10.1016/j.jairtraman.2023.102525openalex-extracted
Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models
inferredcites →https://doi.org/10.1016/j.trb.2015.09.003openalex-extracted
Dynamic control of complex transit systems
inferredcites →https://doi.org/10.1016/j.trc.2018.10.023openalex-extracted
Real time transit demand prediction capturing station interactions and impact of special events
inferredcites →https://doi.org/10.1016/j.trc.2019.06.008openalex-extracted
Bus travel time prediction with real-time traffic information
inferredcites →https://doi.org/10.1016/j.trc.2023.104241openalex-extracted
Deep trip generation with graph neural networks for bike sharing system expansion
inferredcites →https://doi.org/10.1038/s42256-024-00897-5openalex-extracted
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
inferredcites →https://doi.org/10.1061/jtepbs.0000333openalex-extracted
Real-Time Passenger Flow Anomaly Detection Considering Typical Time Series Clustered Characteristics at Metro Stations
inferredcites →https://doi.org/10.1080/13658816.2023.2234959openalex-extracted
Incorporating multimodal context information into traffic speed forecasting through graph deep learning
inferredcites →https://doi.org/10.1080/19427867.2017.1366120openalex-extracted
Real time bus travel time prediction using k-NN classifier
inferredcites →https://doi.org/10.1088/1742-6596/2161/1/012053openalex-extracted
Bus Travel Time Prediction: A Comparative Study of Linear and Non-Linear Machine Learning Models
inferredcites →https://doi.org/10.1109/aidas53897.2021.9574309openalex-extracted
Gaussian Mixture Model - Expectation Maximization Algorithm for Brain Images
inferredcites →https://doi.org/10.1109/icaiccit60255.2023.10466162openalex-extracted
Liquid Neural Networks: A Novel Approach to Dynamic Information Processing
inferredcites →https://doi.org/10.1109/icict60155.2024.10544664openalex-extracted
An Enhanced Real-Time Object Detection Method using Liquid Neural Network and Echo State Network Architecture
inferredcites →https://doi.org/10.1109/jsen.2019.2923982openalex-extracted
A Review of Deep Learning Models for Time Series Prediction
inferredcites →https://doi.org/10.1109/tits.2017.2686877openalex-extracted
A Real-Time Passenger Flow Estimation and Prediction Method for Urban Bus Transit Systems
inferredcites →https://doi.org/10.1109/tits.2018.2815678openalex-extracted
Big Data Analytics in Intelligent Transportation Systems: A Survey
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.2020.3017109openalex-extracted
Short-Term Prediction of Urban Rail Transit Passenger Flow in External Passenger Transport Hub Based on LSTM-LGB-DRS
inferredcites →https://doi.org/10.1109/tits.2020.3019497openalex-extracted
Dynamic Graph Convolution Network for Traffic Forecasting Based on Latent Network of Laplace Matrix Estimation
inferredcites →https://doi.org/10.1109/tits.2023.3257759openalex-extracted
Graph Neural Networks for Intelligent Transportation Systems: A Survey
inferredcites →https://doi.org/10.1109/tits.2023.3319135openalex-extracted
Machine Learning in Urban Rail Transit Systems: A Survey
inferredcites →https://doi.org/10.1109/tkde.2018.2866809openalex-extracted
A Survey on Spatial Prediction Methods
inferredcites →https://doi.org/10.1109/tkde.2021.3133013openalex-extracted
CatGCN: Graph Convolutional Networks With Categorical Node Features
inferredcites →https://doi.org/10.1109/tkde.2023.3333824openalex-extracted
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
inferredcites →https://doi.org/10.1145/3567591openalex-extracted
Constructing Neural Network Based Models for Simulating Dynamical Systems
inferredcites →https://doi.org/10.1155/2015/682390openalex-extracted
Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach
inferredcites →https://doi.org/10.28991/cej-2020-03091566openalex-extracted
Traffic Congestion: Shift from Private Car to Public Transportation
inferredcites →https://doi.org/10.48550/arxiv.2303.11977openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2303.14483openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2407.07218openalex-extracted
inferredcites →https://doi.org/10.58496/bjai/2024/010openalex-extracted
Improving Bus Passenger Flow Prediction Using Bi-LSTM Fusion Model and SMO Algorithm

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-981-96-9914-8_31openalex-snapshot
Overnight Stock Movement Prediction Based on Dynamic Graph Neural Networks
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.