Comparison of models with and without roadway features to estimate annual average daily traffic at non-coverage locations

IF 4.3 Q2 TRANSPORTATION
{"title":"Comparison of models with and without roadway features to estimate annual average daily traffic at non-coverage locations","authors":"","doi":"10.1016/j.ijtst.2023.10.001","DOIUrl":null,"url":null,"abstract":"<div><div>This study develops and evaluates models to estimate annual average daily traffic (AADT) at non-coverage or out-of-network locations. The non-coverage locations are those where counts are performed very infrequently, but an up-to-date and accurate estimate is needed by state departments of transportation. Two types of models are developed, one is that simply uses the nearby known AADT to provide an estimate, the other is that requires roadway features (e.g., type of median, presence of left-turn lane). The advantage of the former type is that no additional data collection is needed, thereby saving time and money for state highway agencies. A natural question that this study seeks to answer is: can this type of model provide equally as good or better estimates than the latter type? The models developed belonging to the first type include hybrid-kriging and Gaussian process regression GPR model (GPR-no-feature), and the models developed belonging to the second type include point-based model, ordinary regression model, quantile regression model, and GPR model (GPR-with-features). The performance of these models is compared against one another using South Carolina data from 2019 to 2021. The results indicate that the GPR-with-features model yields the lowest root mean squared error (RMSE) and lowest mean absolute percentage error (MAPE). It outperforms the hybrid-kriging model by 6.45% in RMSE, GPR without features model by 4.25%, point-based model by 4.69%, regular regression model by 11.35%, and quantile regression model by 4.25%. Similarly, the GPR-with-features model outperforms the hybrid-kriging model by 25.21% in MAPE, GPR without features model by 17.81%, point-based model by 22.26%, regular regression model by 26.36%, and quantile regression model by 21.07%.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 244-259"},"PeriodicalIF":4.3000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Transportation Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2046043023000771","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0

Abstract

This study develops and evaluates models to estimate annual average daily traffic (AADT) at non-coverage or out-of-network locations. The non-coverage locations are those where counts are performed very infrequently, but an up-to-date and accurate estimate is needed by state departments of transportation. Two types of models are developed, one is that simply uses the nearby known AADT to provide an estimate, the other is that requires roadway features (e.g., type of median, presence of left-turn lane). The advantage of the former type is that no additional data collection is needed, thereby saving time and money for state highway agencies. A natural question that this study seeks to answer is: can this type of model provide equally as good or better estimates than the latter type? The models developed belonging to the first type include hybrid-kriging and Gaussian process regression GPR model (GPR-no-feature), and the models developed belonging to the second type include point-based model, ordinary regression model, quantile regression model, and GPR model (GPR-with-features). The performance of these models is compared against one another using South Carolina data from 2019 to 2021. The results indicate that the GPR-with-features model yields the lowest root mean squared error (RMSE) and lowest mean absolute percentage error (MAPE). It outperforms the hybrid-kriging model by 6.45% in RMSE, GPR without features model by 4.25%, point-based model by 4.69%, regular regression model by 11.35%, and quantile regression model by 4.25%. Similarly, the GPR-with-features model outperforms the hybrid-kriging model by 25.21% in MAPE, GPR without features model by 17.81%, point-based model by 22.26%, regular regression model by 26.36%, and quantile regression model by 21.07%.
比较有和无道路特征的模型,以估算非覆盖地点的年平均日交通量
本研究开发并评估了用于估算非覆盖或网络外地点的年平均日交通量 (AADT) 的模型。非覆盖地点是指那些不经常进行统计,但各州交通部门需要最新、准确估计的地点。我们开发了两种模型,一种是简单地使用附近已知的平均车流量来提供估计值,另一种是需要道路特征(如中间分隔带的类型、左转车道的存在)。前者的优点是不需要额外的数据收集,从而为国家公路机构节省了时间和金钱。本研究试图回答的一个自然问题是:这种类型的模型能否提供与后一种类型同样好或更好的估计结果?属于第一种类型的模型包括混合克里金法和高斯过程回归 GPR 模型(无特征 GPR),属于第二种类型的模型包括基于点的模型、普通回归模型、量子回归模型和有特征 GPR 模型(有特征 GPR)。利用南卡罗来纳州 2019 年至 2021 年的数据对这些模型的性能进行了比较。结果表明,带特征的 GPR 模型产生的均方根误差(RMSE)和平均绝对百分比误差(MAPE)最小。它的 RMSE 值比混合克里金模型高出 6.45%,比无特征 GPR 模型高出 4.25%,比基于点的模型高出 4.69%,比常规回归模型高出 11.35%,比量化回归模型高出 4.25%。同样,带特征的 GPR 模型的 MAPE 值比混合克里金模型高出 25.21%,不带特征的 GPR 模型高出 17.81%,基于点的模型高出 22.26%,常规回归模型高出 26.36%,量化回归模型高出 21.07%。
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
International Journal of Transportation Science and Technology
International Journal of Transportation Science and Technology Engineering-Civil and Structural Engineering
CiteScore
7.20
自引率
0.00%
发文量
105
审稿时长
88 days
×
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