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Towards the Design of Smart Vehicular Traffic Flow Prediction

Published: 22 November 2021 Publication History

Abstract

Thanks to the fast development of computing hardware and Machine Learning-based (ML) model, many impressive prediction models have been proposed under the topic of traffic flow prediction. While ML models highly improve the accuracy of the prediction system, it has higher time consumption on the training phase when being applied to a large traffic network, compared to traditional time-series models. The other thing we should consider when predicting the traffic flow in a large traffic network is to utilize the spatial correlation among the detectors. To solve above problems, we will provide a traffic flow prediction solution in this paper. The solution has three parts: a hybrid prediction model based on Graph Convolutional Network (GCN) and Recurrent Neural Network (RNN), which can extract spatial-temporal features from dataset; a prediction strategy for multi-step prediction; an efficient training strategy for prediction on large-scale network.

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Cited By

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  • (2024)Spatio-temporal graph learning: Traffic flow prediction of mobile edge computing in 5G/6G vehicular networksComputer Networks10.1016/j.comnet.2024.110676252(110676)Online publication date: Oct-2024
  • (2023)A Novel Traffic Characteristics Aware and Context Prediction Protocol for Intelligent Connected VehiclesIEEE Transactions on Vehicular Technology10.1109/TVT.2023.325990372:8(9897-9908)Online publication date: Aug-2023
  • (2022)Is the Remote ID a Threat to the Drone's Location Privacy on the Internet of Drones?Proceedings of the 20th ACM International Symposium on Mobility Management and Wireless Access on 20th ACM International Symposium on Mobility Management and Wireless Access10.1145/3551660.3560914(81-88)Online publication date: 24-Oct-2022

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cover image ACM Conferences
MobiWac '21: Proceedings of the 19th ACM International Symposium on Mobility Management and Wireless Access
November 2021
175 pages
ISBN:9781450390798
DOI:10.1145/3479241
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 November 2021

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Author Tags

  1. hybrid model
  2. intelligent transportation system
  3. large-scale network
  4. machine learning
  5. prediction
  6. traffic flow

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Overall Acceptance Rate 83 of 272 submissions, 31%

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Cited By

View all
  • (2024)Spatio-temporal graph learning: Traffic flow prediction of mobile edge computing in 5G/6G vehicular networksComputer Networks10.1016/j.comnet.2024.110676252(110676)Online publication date: Oct-2024
  • (2023)A Novel Traffic Characteristics Aware and Context Prediction Protocol for Intelligent Connected VehiclesIEEE Transactions on Vehicular Technology10.1109/TVT.2023.325990372:8(9897-9908)Online publication date: Aug-2023
  • (2022)Is the Remote ID a Threat to the Drone's Location Privacy on the Internet of Drones?Proceedings of the 20th ACM International Symposium on Mobility Management and Wireless Access on 20th ACM International Symposium on Mobility Management and Wireless Access10.1145/3551660.3560914(81-88)Online publication date: 24-Oct-2022
  • (2022)MixRide: An Energy-Aware Location Privacy Protection Mechanism for the Internet of DronesGLOBECOM 2022 - 2022 IEEE Global Communications Conference10.1109/GLOBECOM48099.2022.10001141(3527-3532)Online publication date: 4-Dec-2022

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