Creation of Truck Weight Road Groups for Pavement Design
Publication: Construction Research Congress 2024
ABSTRACT
Weigh-in-motion (WIM) and automatic traffic recorder (ATR) devices, particularly the former, serve as indispensable sources of traffic data for mechanistic-empirical (ME) pavement design. However, the sparsely distributed WIM and ATR devices are unable to cover the entire roadway network in Indiana. The current truck weight road groups (TWRG) were developed using WIM traffic data of 20 years ago to reflect regional traffic conditions in Indiana. Because of the outdated traffic data, the existing TWRG classes fail to accurately reflect the actual traffic conditions, especially in the cases of extremely low and high truck traffic volumes. This study was performed to derive new TWRG classes using the newly collected WIM traffic data to provide accurate traffic information for pavement design. Consequently, employing the new TWRG values can significantly improve the quality of pavement design, resulting in considerable cost reduction of highway projects.
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REFERENCES
AASHTO. 2008. Mechanistic-Empirical Pavement Design Guide, interim edition: A manual of practice. Washington, DC.
Abbas, A., Frankhouser, A., and Papagiannakis, A. 2014. “Effect of Traffic Load Input Level on Mechanistic-Empirical Pavement Design.” Transportation Research Record: Journal of Transportation Research Board, vol. (2443), pp. 63–77. DOI: https://doi.org/10.3141/2443-08.
Bao, J., Hu, X., Peng, C., Jiang, Y., Li, S., and Nantung, T. 2020. Truck Traffic and Load Spectra of Indiana Roadways for the Mechanistic-Empirical Pavement Design Guide. West Lafayette, IN: Purdue University. https://doi.org/10.5703/1288284317227.
Bao, J., Hu, X., Jiang, Y., and Li, S. 2022a. “A Convolutional Neural Network Model for Identifying Unclassified and Misclassified Vehicles Using Spatial Pyramid Pooling.” Proceedings of the ASCE Construction Research Congress (pp. 956–966), Arlington, Virginia, March 9-12, 2022. https://doi.org/10.1061/9780784483961.100.
Bao, J., Hu, X., Jiang, Y., and Li, S. 2022b. “A Machine Learning Approach to Improving Accuracy of WIM Traffic Data.” Proceedings of the ASCT International Conference on Transportation & Development. Seattle, Washington, May 31- June 3, 2022.
Cheng, P., Hu, X., Bao, J., Jiang, Y., Li, S., and Nantung, T. 2022. “Traffic input module for mechanistic-empirical pavement design with weigh-in-motion data.” International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2022.09.001.
FHWA (Federal Highway Administration). 2016. Traffic monitoring guide, pp 18–22. Washington, D.C.
Hasan, M. A., Islam, M. R., and Tarefder, R. A. 2016. “Clustering vehicle class distribution and axle load spectra for mechanistic-empirical predicting pavement performance.” Journal of Transportation Engineering, 142(11), 1–11. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000876.
Hu, X., Bao, J., Jiang, Y., Li, S., and Nantung, T. 2020. “Truck Traffic and Truck Axle Load Spectra on Interstate Highways for Mechanistic-Empitical Pavement Design.” Proceedings of the ASCE Construction Research Congress (pp. 380–388). Phoenix, Arizona, March 8-10, 2020. https://doi.org/10.1061/9780784482865.041.
INDOT (Indiana Department of Transportation). 2013. Indiana Design Manual -2013. Indianapolis, IN.
Jasim, A., Wang, H., and Bennert, T. 2019. “Evaluation of Clustered Traffic Inputs for Mechanistic-Empirical Pavement Design: Case Study in New Jersey.” Transportation Research Record: Journal of the Transportation Research Board, vol. 2673(11), pp. 332–348. DOI: https://doi.org/10.1177/0361198119853557.
Lu, Q., Zhang, Y., and Harvey, J. 2009. “Estimation of Truck Traffic Inputs for Mechanistic-Empirical Pavement Design in California.” Transportation Research Record: Journal of the Transportation Research Board, vol. 2095, pp. 62–72. https://journals.sagepub.com/doi/pdf/10.3141/2095-07.
Nantung, T. E. 2010. “Implementing the Mechanistic-Empirical Pavement Design Guide for Cost Savings in Indiana.” Transportation Research News 271. http://onlinepubs.trb.org/onlinepubs/trnews/trnews271rpo.pdf.
Oh, J., Walubita, L., and Leidy, J. 2015. “Establishment of Statewide Axle Load Spectra Data using Cluster Analysis.” KSCE Journal of Civil Engineering, vol. 19(7), pp. 2083–2090. DOI https://doi.org/10.1007/s12205-014-0374-9.
Papagiannakis, A., Bracher, M., and Jackson, N. 2006. “Utilizing Clustering Techniques in Estimating Traffic Data Input for Pavement Design.” Journal of Transportation Engineering, vol. 132(11), pp. 872–879. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:11(872).
US Census Bureau. 2023. Total Construction Spending: Highway and Street in the United States [TLHWYCONS]. Retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/TLHWYCONS, March 23, 2023.
Yang, G., Li, Q., Wang, K., Fei, Y., and Wang, C. 2017. “Multiway-Based Weigh-in-Motion Data-Clustering Analysis for Pavement ME Design.” Journal of Computing in Civil Engineering, vol. 31(5). DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000688.
Walubita, L. F., Fuentes, L., Faruk, A. N. M., Komba, J. J., Prakoso, A., and Naik, B. 2020. “Mechanistic-empirical Compatible Traffic Data Generation: Portable Weigh-in-Motion versus Cluster Analysis.” Journal of Testing and Evaluation, 48(3), 2377–2392. https://doi.org/10.1520/JTE20190745.
Washington, S. P., Karlaftis, M. G., and Mannering, F. L. 2003. Statistical and Econometric Methods for Transportation Data Analysis, CRC press, New York.
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Construction Research Congress 2024
Pages: 679 - 689
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Published online: Mar 18, 2024
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