On the public citation graph

A Novel Privacy-Aware Traffic Speed Prediction Model Based on Recurrent Neural Networks

https://doi.org/10.2139/ssrn.4591259
0 signed · 28 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/978-3-319-67786-6_6openalex-extracted
Differentially Private Empirical Risk Minimization with Input Perturbation
inferredcites →https://doi.org/10.1007/s12204-021-2382-5openalex-extracted
Federated Approach for Privacy-Preserving Traffic Prediction Using Graph Convolutional Network
inferredcites →https://doi.org/10.1016/j.aej.2016.04.031openalex-extracted
Development of traffic stream characteristics models for intercity roads in Egypt
inferredcites →https://doi.org/10.1016/j.aej.2020.09.038openalex-extracted
The effect of the dataset on evaluating urban traffic prediction
inferredcites →https://doi.org/10.1016/j.future.2020.12.003openalex-extracted
Privacy-preserving blockchain-based federated learning for traffic flow prediction
inferredcites →https://doi.org/10.1016/j.trc.2016.10.019openalex-extracted
Short-term speed predictions exploiting big data on large urban road networks
inferredcites →https://doi.org/10.1016/j.trc.2018.12.004openalex-extracted
Enhancing transportation systems via deep learning: A survey
inferredcites →https://doi.org/10.1016/j.trc.2019.04.022openalex-extracted
Quantifying privacy vulnerability of individual mobility traces: A case study of license plate recognition data
inferredcites →https://doi.org/10.1016/j.trc.2019.05.031openalex-extracted
A dual model/artificial neural network framework for privacy analysis in traffic monitoring systems
inferredcites →https://doi.org/10.1016/j.trc.2019.09.008openalex-extracted
An effective spatial-temporal attention based neural network for traffic flow prediction
inferredcites →https://doi.org/10.1016/j.trpro.2019.05.041openalex-extracted
Optimal privacy control for transport network data sharing
inferredcites →https://doi.org/10.1016/j.vehcom.2019.100187openalex-extracted
EGeoIndis: An effective and efficient location privacy protection framework in traffic density detection
inferredcites →https://doi.org/10.1109/cisce55963.2022.9851055openalex-extracted
Federated Learning-based Privacy-Preserving Traffic Flow Prediction Scheme for VANETs
inferredcites →https://doi.org/10.1109/innocit.2017.8319153openalex-extracted
Location based service for presence system using haversine method
inferredcites →https://doi.org/10.1109/itsc45102.2020.9294453openalex-extracted
FedGRU: Privacy-preserving Traffic Flow Prediction via Federated Learning
inferredcites →https://doi.org/10.1109/iwcmc48107.2020.9148111openalex-extracted
Federated Learning for RSS Fingerprint-based Localization: A Privacy-Preserving Crowdsourcing Method
inferredcites →https://doi.org/10.1109/jiot.2020.2991401openalex-extracted
Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach
inferredcites →https://doi.org/10.1109/tii.2021.3055283openalex-extracted
FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting
inferredcites →https://doi.org/10.1109/tits.2020.3032882openalex-extracted
Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction
inferredcites →https://doi.org/10.1109/tits.2022.3157056openalex-extracted
FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction
inferredcites →https://doi.org/10.1109/tits.2022.3179391openalex-extracted
Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning
inferredcites →https://doi.org/10.1145/3391707openalex-extracted
A Traffic Density Estimation Model Based on Crowdsourcing Privacy Protection
inferredcites →https://doi.org/10.17762/ijritcc.v6i7.1690openalex-extracted
A Landmark Based Shortest Path Detection by Using A* and Haversine Formula
inferredcites →https://doi.org/10.20532/ccvw.2015.0002openalex-extracted
Measurement of Road Traffic Parameters based on Multi-Vehicle Tracking
inferredcites →https://doi.org/10.3390/info13080381openalex-extracted
Prediction and Privacy Scheme for Traffic Flow Estimation on the Highway Road Network
inferredcites →https://doi.org/10.48550/arxiv.1710.07425openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1801.02143openalex-extracted
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction
inferredcites →https://doi.org/10.48550/arxiv.2004.12254openalex-extracted
Privacy in Deep Learning: A Survey
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.