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%.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Transportation Science and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.