NeuroSync:一种用于5G及以后车辆交通数据时间序列预测的新型神经网络架构

IF 1.7 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Timothy Dkhar, Chandrasen Pandey, Sharmila A. J. Francis, Diptendu Sinha Roy, Ashish Kr Luhach
{"title":"NeuroSync:一种用于5G及以后车辆交通数据时间序列预测的新型神经网络架构","authors":"Timothy Dkhar,&nbsp;Chandrasen Pandey,&nbsp;Sharmila A. J. Francis,&nbsp;Diptendu Sinha Roy,&nbsp;Ashish Kr Luhach","doi":"10.1002/dac.70035","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>The efficient management and prediction of urban traffic flow are paramount in the age of beyond 5G smart cities and advanced transportation systems. Traditional methods often fail to handle the nonlinear and dynamic nature of traffic data, necessitating more advanced solutions. This paper introduces <i>NeuroSync</i>, a novel neural network architecture designed to leverage the strengths of spiking neuron layers and gated recurrent units (GRUs) combined with temporal pattern attention mechanisms to effectively forecast traffic patterns. The architecture is specifically tailored to address the complexities inherent in nonstationary urban traffic datasets, capturing both spatial and temporal relationships within the data. <i>NeuroSync</i> not only outperforms traditional forecasting models such as ARIMA and exponential smoothing but also shows significant improvement over contemporary neural network approaches like LSTM, CNN, Seq2Seq, RNN, GRU, Transformer, and Autoencoder in terms of mean squared error (MSE) and mean absolute error (MAE). The model's efficacy is demonstrated through extensive experiments with real-world traffic data, underscoring its potential to enhance urban mobility management and support the infrastructure of intelligent transportation systems.</p>\n </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 6","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NeuroSync: A Novel Neural Network Architecture for Time Series Forecasting of Vehicle Traffic Data Over 5G and Beyond\",\"authors\":\"Timothy Dkhar,&nbsp;Chandrasen Pandey,&nbsp;Sharmila A. J. Francis,&nbsp;Diptendu Sinha Roy,&nbsp;Ashish Kr Luhach\",\"doi\":\"10.1002/dac.70035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>The efficient management and prediction of urban traffic flow are paramount in the age of beyond 5G smart cities and advanced transportation systems. Traditional methods often fail to handle the nonlinear and dynamic nature of traffic data, necessitating more advanced solutions. This paper introduces <i>NeuroSync</i>, a novel neural network architecture designed to leverage the strengths of spiking neuron layers and gated recurrent units (GRUs) combined with temporal pattern attention mechanisms to effectively forecast traffic patterns. The architecture is specifically tailored to address the complexities inherent in nonstationary urban traffic datasets, capturing both spatial and temporal relationships within the data. <i>NeuroSync</i> not only outperforms traditional forecasting models such as ARIMA and exponential smoothing but also shows significant improvement over contemporary neural network approaches like LSTM, CNN, Seq2Seq, RNN, GRU, Transformer, and Autoencoder in terms of mean squared error (MSE) and mean absolute error (MAE). The model's efficacy is demonstrated through extensive experiments with real-world traffic data, underscoring its potential to enhance urban mobility management and support the infrastructure of intelligent transportation systems.</p>\\n </div>\",\"PeriodicalId\":13946,\"journal\":{\"name\":\"International Journal of Communication Systems\",\"volume\":\"38 6\",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2025-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Communication Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/dac.70035\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Communication Systems","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/dac.70035","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

在超越5G的智慧城市和先进交通系统时代,高效管理和预测城市交通流量至关重要。传统的方法往往无法处理交通数据的非线性和动态性,需要更先进的解决方案。本文介绍了一种新的神经网络架构NeuroSync,该架构旨在利用峰值神经元层和门控循环单元(gru)的优势,结合时间模式注意机制,有效地预测交通模式。该架构是专门为解决非平稳城市交通数据集固有的复杂性而量身定制的,可以捕获数据中的空间和时间关系。NeuroSync不仅优于ARIMA和指数平滑等传统预测模型,而且在均方误差(MSE)和平均绝对误差(MAE)方面,也比LSTM、CNN、Seq2Seq、RNN、GRU、Transformer和Autoencoder等当代神经网络方法有了显著的改进。该模型的有效性通过对真实交通数据的广泛实验得到了证明,强调了其在加强城市交通管理和支持智能交通系统基础设施方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NeuroSync: A Novel Neural Network Architecture for Time Series Forecasting of Vehicle Traffic Data Over 5G and Beyond

The efficient management and prediction of urban traffic flow are paramount in the age of beyond 5G smart cities and advanced transportation systems. Traditional methods often fail to handle the nonlinear and dynamic nature of traffic data, necessitating more advanced solutions. This paper introduces NeuroSync, a novel neural network architecture designed to leverage the strengths of spiking neuron layers and gated recurrent units (GRUs) combined with temporal pattern attention mechanisms to effectively forecast traffic patterns. The architecture is specifically tailored to address the complexities inherent in nonstationary urban traffic datasets, capturing both spatial and temporal relationships within the data. NeuroSync not only outperforms traditional forecasting models such as ARIMA and exponential smoothing but also shows significant improvement over contemporary neural network approaches like LSTM, CNN, Seq2Seq, RNN, GRU, Transformer, and Autoencoder in terms of mean squared error (MSE) and mean absolute error (MAE). The model's efficacy is demonstrated through extensive experiments with real-world traffic data, underscoring its potential to enhance urban mobility management and support the infrastructure of intelligent transportation systems.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.90
自引率
9.50%
发文量
323
审稿时长
7.9 months
期刊介绍: The International Journal of Communication Systems provides a forum for R&D, open to researchers from all types of institutions and organisations worldwide, aimed at the increasingly important area of communication technology. The Journal''s emphasis is particularly on the issues impacting behaviour at the system, service and management levels. Published twelve times a year, it provides coverage of advances that have a significant potential to impact the immense technical and commercial opportunities in the communications sector. The International Journal of Communication Systems strives to select a balance of contributions that promotes technical innovation allied to practical relevance across the range of system types and issues. The Journal addresses both public communication systems (Telecommunication, mobile, Internet, and Cable TV) and private systems (Intranets, enterprise networks, LANs, MANs, WANs). The following key areas and issues are regularly covered: -Transmission/Switching/Distribution technologies (ATM, SDH, TCP/IP, routers, DSL, cable modems, VoD, VoIP, WDM, etc.) -System control, network/service management -Network and Internet protocols and standards -Client-server, distributed and Web-based communication systems -Broadband and multimedia systems and applications, with a focus on increased service variety and interactivity -Trials of advanced systems and services; their implementation and evaluation -Novel concepts and improvements in technique; their theoretical basis and performance analysis using measurement/testing, modelling and simulation -Performance evaluation issues and methods.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信