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

作　　者：Jing Wang Ryan DeVine Nathan Huynh Weimin Jin Gurcan Comert Mashrur Chowdhury 

机构地区：[1]Department of Civil&Environmental Engineering,University of Nebraska-Lincoln,Lincoln,NE 68583-0851,USA [2]Rummel,Klepper,&Kahl,LLP,Richmond,VA 23223,USA [3]Arcadis,10205 Westheimer Road,Suite 800,Houston,TX 77042,USA [4]Department of Computer Science,Physics,and Engineering,Benedict College,Columbia,SC 29204,USA [5]Glenn Department of Civil Engineering,Clemson University,216 Lowry Hall,Clemson,SC 29631,USA

出　　处：《International Journal of Transportation Science and Technology》2024年第3期244-259,共16页交通科学与技术(英文)

基　　金：supported by the South Carolina Department of Transportation under the State Planning and Research grant(No.SPR 749).

摘　　要：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 road-way 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 regres-sion 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 regres-sion 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%.

关 键 词：Annual average daily traffic(AADT) Non-coverage roads Kriging method Point-based model Gaussian process regression(GPR) 

分 类 号：O17[理学—数学]