Short-term traffic forecasting model – prevailing trends and guidelines

IF 2.7 4区 工程技术 Q2 TRANSPORTATION SCIENCE & TECHNOLOGY
Kian Lun Soon, Robin Kuok Cheong Chan, J. Lim, R. Parthiban
{"title":"Short-term traffic forecasting model – prevailing trends and guidelines","authors":"Kian Lun Soon, Robin Kuok Cheong Chan, J. Lim, R. Parthiban","doi":"10.1093/tse/tdac058","DOIUrl":null,"url":null,"abstract":"\n The design parameters serve as an integral part of developing a robust short-term traffic forecasting model. These parameters include scope determination, input data preparation, output parameters, and modelling techniques. This paper takes a further leap to analyse the recent trend of design parameters through a Systematic Literature Review (SLR) based on peer-reviewed articles up to 2021. The key important findings are summarised along with the challenges to performing short-term traffic forecasting. Intuitively, this paper offers insights into the next wave of research that contributes significantly to industries.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Safety and Environment","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1093/tse/tdac058","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The design parameters serve as an integral part of developing a robust short-term traffic forecasting model. These parameters include scope determination, input data preparation, output parameters, and modelling techniques. This paper takes a further leap to analyse the recent trend of design parameters through a Systematic Literature Review (SLR) based on peer-reviewed articles up to 2021. The key important findings are summarised along with the challenges to performing short-term traffic forecasting. Intuitively, this paper offers insights into the next wave of research that contributes significantly to industries.
短期交通预测模型-主要趋势及指引
设计参数是开发稳健的短期交通预测模型的组成部分。这些参数包括范围确定、输入数据准备、输出参数和建模技术。本文通过基于截至2021年同行评审文章的系统文献综述(SLR),进一步分析了设计参数的最新趋势。总结了关键的重要发现以及执行短期交通预测的挑战。直观地说,这篇论文为下一波对行业有重大贡献的研究提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Transportation Safety and Environment
Transportation Safety and Environment TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
3.90
自引率
13.60%
发文量
32
审稿时长
10 weeks
×
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