CART Algorithm: A Data-Driven Approach to Automate Maintenance Selection in Pavement Management Systems
Publication: Journal of Infrastructure Systems
Volume 28, Issue 3
Abstract
One of the essential components for developing a plan to manage pavements is the maintenance selection step, which is done using decision trees (DTs). In most cases, DTs are developed based on experts’ judgment. These trees are subjective and result in inconsistent decisions over time. A more objective approach is to use the data-driven trees which was addressed in multiple research studies. However, the resulting trees were limited in terms of the prediction accuracy levels achieved, the types of pavements covered, and the predicted maintenance action types. The goal of this paper is to use pavement management system (PMS) data to improve the consistency of the decision-making process in PMS to make more accountable decisions. To achieve this goal, this paper’s main objectives are to utilize the classification and regression trees (CART) algorithm to create a data-driven maintenance selection DT model and to develop a data-driven model impact evaluation approach to assess the benefit and cost of using the data-driven DT. An extensive dataset covering the three pavement types in the state of Iowa is used to achieve this objective. Also, a possible approach to evaluate the impact of using data-driven DTs to select maintenance actions instead of the subjective DTs is investigated. With 10 internal nodes and 52.5% overall prediction accuracy of five maintenance action types, the final DT model developed in this paper showed an improvement in the prediction accuracy and model complexity compared with the trees presented in the literature. This model is also expected to save the agency an average of $3.9 million compared with using the PMS output.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The list of available models, codes, and data is listed below:
1.
The codes of the decision tree model and model impact evaluation are available upon request
2.
The PMIS data are available online on Iowa DOT open data website.
References
Abdelaty, A., H. D. Jeong, O. G. Smadi, and D. D. Gransberg. 2015. Iowa pavement asset management decision-making framework. Ames: Iowa State Univ.
Abdelrahim, A. M., and K. P. George. 2000. “Artificial neural network for enhancing selection of pavement maintenance strategy.” Transp. Res. Rec. 1699 (1): 16–22. https://doi.org/10.3141/1699-03.
Abukhalil, Y. 2019. Cross asset resource allocation framework for pavement and bridges in Iowa. Ames: Iowa State Univ.
Baladi, G. Y., T. Dawson, G. Musunuru, M. Prohaska, and K. Thomas. 2017. Pavement performance measures and forecasting and the effects of maintenance and rehabilitation strategy on treatment effectiveness. McLean, VA: Federal Highway Administration.
Chen, C.-T., C.-T. Hung, J.-D. Lin, and P.-H. Sung. 2015. “Application of a decision tree method with a spatiotemporal object database for pavement maintenance and management.” J. Mar. Sci. Technol. 23 (3): 302–307. https://doi.org/10.6119/JMST-014-0327-5.
de León Izeppi, E., A. Morrison, G. W. Flintsch, and K. K. McGhee. 2015. Best practices and performance assessment for preventive maintenance treatments for Virginia pavements. Charlottesville, VA: Virginia Center for Transportation Innovation and Research.
Dojutrek, M. S., P. A. Makwana, and S. Labi. 2012. “A methodology for highway asset valuation in Indiana.” In Joint transportation research program. West Lafayette, IN: Purdue Univ.
Entezari-Maleki, R., A. Rezaei, and B. Minaei-Bidgoli. 2009. “Comparison of classification methods based on the type of attributes and sample size.” J. Convergence Inf. Technol. 4 (3): 94–102. https://doi.org/10.4156/jcit.vol4.issue3.14.
Gao, H., and X. Zhang. 2013. “A Markov-based road maintenance optimization model considering user costs.” Comput.-Aided Civ. Infrastruct. Eng. 28 (6): 451–464. https://doi.org/10.1111/mice.12009.
Han, J., M. Kamber, and J. Pei. 2011. “Data mining concepts and techniques third edition.” Morgan Kaufmann Ser. Data Manage. Syst. 5 (4): 83–124.
Hicks, R. G., K. Dunn, and J. S. Moulthrop. 1997. “Framework for selecting effective preventive maintenance treatments for flexible pavements.” Transp. Res. Rec. 1597 (1): 1–10. https://doi.org/10.3141/1597-01.
Hicks, R. G., J. S. Moulthrop, and J. Daleiden. 1999. “Selecting a preventive maintenance treatment for flexible pavements.” Transp. Res. Rec. 1680 (1): 1–12. https://doi.org/10.3141/1680-01.
Johnson, A. M. 2000. Best practices handbook on asphalt pavement maintenance. Minneapolis, MN: Minnesota Technology Transfer/LTAP Program Center for Transportation Studies.
Labi, S., G. Lamptey, and S.-H. Kong. 2007. “Effectiveness of microsurfacing treatments.” J. Transp. Eng. 133 (5): 298–307. https://doi.org/10.1061/(ASCE)0733-947X(2007)133:5(298).
Labi, S., and K. C. Sinha. 2003. “Measures of short-term effectiveness of highway pavement maintenance.” J. Transp. Eng. 129 (6): 673–683. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(673).
Labi, S., and K. C. Sinha. 2004. “Effectiveness of highway pavement seal coating treatments.” J. Transp. Eng. 130 (1): 14–23. https://doi.org/10.1061/(ASCE)0733-947X(2004)130:1(14).
Lu, P., and D. Tolliver. 2012. “Pavement treatment short-term effectiveness in IRI change using long-term pavement program data.” J. Transp. Eng. 138 (11): 1297–1302. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000446.
Maimon, O., and L. Rokach. 2005. Data mining and knowledge discovery handbook. New York: Springer.
Miller, J. S., and W. Y. Bellinger. 2003. Distress identification manual for the long-term pavement performance program. McLean, VA: Federal Highway Administration. Office of Infrastructure Research and Development.
Onyango, M., S. A. Merabti, J. Owino, I. Fomunung, and W. Wu. 2018. “Analysis of cost-effective pavement treatment and budget optimization for arterial roads in the city of Chattanooga.” Front. Struct. Civ. Eng. 12 (3): 291–299. https://doi.org/10.1007/s11709-017-0419-5.
Pour, S. A., and D. H. S. Jeong. 2012. “Realistic life-cycle cost analysis with typical sequential patterns of pavement treatment through association analysis.” Transp. Res. Rec. 2304 (1): 104–111. https://doi.org/10.3141/2304-12.
Shahin, M. Y. 2005. Pavement management for airports, roads, and parking lots. Boston: Springer New York.
Smadi, O. G. 2000. “Knowledge based expert system pavement management optimization.” Accessed September 1, 2021. http://lib.dr.iastate.edu/.
Song, Y.-Y., and L. U. Ying. 2015. “Decision tree methods: Applications for classification and prediction.” Shanghai Arch Psychiatry 27 (2): 130. https://doi.org/10.11919/j.issn.1002-0829.215044.
Therneau, T., B. Atkinson, B. Ripley, and M. B. Ripley. 2015. “rpart: Recursive partitioning and regression trees. R package version 4.1-15.” https://CRAN.R-project.org/package=rpart.
Therneau, T. M., and E. J. Atkinson. 1997. An introduction to recursive partitioning using the RPART routines. Rochester, MN: Mayo Foundation.
Zhou, G., L. Wang, D. Wang, and S. Reichle. 2010. “Integration of GIS and data mining technology to enhance the pavement management decision making.” J. Transp. Eng. 136 (4): 332–341. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000092.
Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 28 • Issue 3 • September 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 1, 2021
Accepted: Feb 7, 2022
Published online: Jun 17, 2022
Published in print: Sep 1, 2022
Discussion open until: Nov 17, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Yazan Abukhalil, Mohamed S. Yamany, Omar Smadi, Detection of Unreported Treatments in Pavement Management System of Iowa DOT Using Machine Learning Classification Algorithm, Journal of Transportation Engineering, Part B: Pavements, 10.1061/JPEODX.0000400, 148, 4, (2022).