Traffic volume information has long played an important role in many transportation related works,such as traffic operations,roadway design,air quality control,and policy making.However,monitoring traffic volumes over...Traffic volume information has long played an important role in many transportation related works,such as traffic operations,roadway design,air quality control,and policy making.However,monitoring traffic volumes over a large spatial area is not an easy task due to the significant amount of time and manpower required to collect such large-scale datasets.In this study,a hybrid geostatistical approach,named Network Regression Kriging,has been developed to estimate urban traffic volumes by incorporating auxiliary variables such as road type,speed limit,and network accessibility.Since standard kriging is based on Euclidean distances,this study implements road network distances to improve traffic volumes estimations.A case study using 10-year of traffic volume data collected within the city of Edmonton was conducted to demonstrate the robustness of the model developed herein.Results suggest that the proposed hybrid model significantly outperforms the standard kriging method in terms of accuracy by 4.0%overall,especially for a large-scale network.It was also found that the necessary stationarity assumption for kriging did not hold true for a large network whereby separate estimations for each road type performed significantly better than a general estimation for the overall network by 4.12%.展开更多
文摘Traffic volume information has long played an important role in many transportation related works,such as traffic operations,roadway design,air quality control,and policy making.However,monitoring traffic volumes over a large spatial area is not an easy task due to the significant amount of time and manpower required to collect such large-scale datasets.In this study,a hybrid geostatistical approach,named Network Regression Kriging,has been developed to estimate urban traffic volumes by incorporating auxiliary variables such as road type,speed limit,and network accessibility.Since standard kriging is based on Euclidean distances,this study implements road network distances to improve traffic volumes estimations.A case study using 10-year of traffic volume data collected within the city of Edmonton was conducted to demonstrate the robustness of the model developed herein.Results suggest that the proposed hybrid model significantly outperforms the standard kriging method in terms of accuracy by 4.0%overall,especially for a large-scale network.It was also found that the necessary stationarity assumption for kriging did not hold true for a large network whereby separate estimations for each road type performed significantly better than a general estimation for the overall network by 4.12%.
