Snowplow Truck Performance Assessment and Feature Importance Analysis Using Machine-Learning Techniques
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 2
Abstract
Snowplow trucks serve a crucial role in winter maintenance activities by removing, loading, and disposing of snow. A performance-monitoring and analysis process can assist transportation agencies in effectively managing snowplow trucks and maintaining the normal functioning of roadways. Previous literature suggests that most snowplow truck performance analysis is done through life-cycle cost assessment at the macro level to determine the optimal life cycle for the entire truck fleet. However, this can lead to a waste of resources and may incur bias due to ignorance of performance variations resulting from endogenous and exogenous features. More important, such analysis fails to identify the factors that contribute to performance deterioration. With the proliferation of operational data on snowplow operations, these concerns can be addressed through predictive machine-learning (ML) techniques in a data-driven fashion. In this study, we applied a popular ML technique, random forest (RF), to predict the performance of snowplow trucks, which was quantified via the rank of major repair times. Another ML technique, linear support vector machine (SVM), was also applied for benchmarking and comparison. Using the snowplow truck fleet managed by the Utah Department of Transportation (UDOT), both models were implemented and it was demonstrated that RF outperforms linear SVM with regard to prediction accuracy. Further, a feature importance analysis from the RF model can help transportation agencies to improve truck replacement strategies by identifying crucial performance factors. Lastly, a sample application of the developed prediction model using RF suggests the threshold of work intensity for preventing the rapid deterioration of truck performance in various working environments. Compared with the life-cycle cost analyses used in previous studies, the prediction model proposed here can help transportation agencies to better prioritize fleet replacement.
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.
Acknowledgments
This article is based on the research project Class 8 Snowplow Truck Performance Assessment, jointly sponsored by UDOT and the Mountain Plain Consortium (MPC) of USDOT University Transportation Centers program. Special thanks go to Tim Ularich, Vincent Liu, Nicole Godfrey, Jeff Casper, and Jack Mason for their support and feedback on this study. The work presented in this article remains the sole responsibility of the authors.
References
Adams, T. M., M. Danijarsa, T. Martinelli, G. Stanuch, and A. Vonderohe. 2003. “Performance measures for winter operations.” Transp. Res. Rec. 1824 (1): 87–97. https://doi.org/10.3141/1824-10.
Alpaydin, E. 2009. “Introduction and supervised learning.” In Introduction to machine learning, 1–44. Cambridge, MA: MIT Press.
Breiman, L. 1984. Classification and regression trees. New York: Routledge.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (1): 5–32.
Bukhsh, Z. A., A. Saeed, I. Stipanovic, and A. G. Doree. 2019. “Predictive maintenance using tree-based classification techniques: A case of railway switches.” Transp. Res. Part C: Emerging Technol. 101 (Apr): 35–54. https://doi.org/10.1016/j.trc.2019.02.001.
Cortes, C., and V. Vapnik. 1995. “Support-vector networks.” Mach. Learn. 20 (3): 273–297.
CTPP (Census Transportation Planning Products Program). 2019. “Special tabulations of American Community Survey data.” Accessed February 1, 2019. http://data5.ctpp.transportation.org/ctpp/Browse/browsetables.aspx.
Dietterich, T. G. 2000. “Ensemble methods in machine learning.” In Proc., Int. Workshop on Multiple Classifier Systems, 1–15. Berlin: Springer.
Fayyaz, S. S. K., C. X. Liu, and R. Wei. 2018. “Transit vehicle performance analysis for service continuity/termination: A data envelopment analysis approach.” Transp. Res. Rec. 2672 (8): 511–522. https://doi.org/10.1177/0361198118772725.
FHWA (Federal Highway Administration). 2016. Road weather management program—Snow and ice. Washington, DC: FHWA.
Hajibabai, L., S. M. Nourbakhsh, Y. Ouyang, and F. Peng. 2014. “Network routing of snowplow trucks with resource replenishment and plowing priorities: Formulation, algorithm, and application.” Transp. Res. Rec. 2440 (1): 16–25. https://doi.org/10.3141/2440-03.
Hall, S. A., J. S. Kaufman, and T. C. Ricketts. 2006. “Defining urban and rural areas in US epidemiologic studies.” J. Urban Health 83 (2): 162–175. https://doi.org/10.1007/s11524-005-9016-3.
Kohavi, R. 1995. “A study of cross-validation and bootstrap for accuracy estimation and model selection.” IJCAI 14 (2): 1137–1145.
Kotsiantis, S. B., D. Kanellopoulos, and P. E. Pintelas. 2006. “Data preprocessing for supervised learning.” Int. J. Comput. Sci. 1 (2): 111–117.
Kwon, T. J., and L. Gu. 2017. “Modelling of winter road surface temperature (RST)—A GIS-based approach.” In Proc., 2017 4th Int. Conf. on Transportation Information and Safety (ICTIS). New York: IEEE. https://doi.org/10.1109/ICTIS.2017.804782.
Litman, T. 1998. “Transportation cost and benefit analysis: Techniques, estimates and implications.” Transp. Res. Rec. 1649: 86–94.
Marković, N., S. Milinković, K. S. Tikhonov, and P. Schonfeld. 2015. “Analyzing passenger train arrival delays with support vector regression.” Transp. Res. Part C: Emerging Technol. 56 (Jul): 251–262. https://doi.org/10.1016/j.trc.2015.04.004.
Mayer, G. J. 1947. “Declining balance depreciation.” Taxes 25 (1): 162.
MesoWest. 2019. “MesoWest Data, the University of Utah.” Accessed February 1, 2019. https://mesowest.utah.edu/.
Perrier, N., A. Langevin, and J. F. Campbell. 2006. “A survey of models and algorithms for winter road maintenance. Part I: System design for spreading and plowing.” Comput. Oper. Res. 33 (Jan): 209–238. https://doi.org/10.1016/j.cor.2004.07.006.
Quinlan, J. R. 1986. “Induction of decision trees.” Mach. Learn. 1 (1): 81–106.
Quinlan, J. R. 2014. C4.5: Programs for machine learning. St. Louis: Elsevier.
Rodriguez, J. D., A. Perez, and J. A. Lozano. 2009. “Sensitivity analysis of -fold cross validation in prediction error estimation.” IEEE Trans. Pattern Anal. Mach. Intell. 32 (3): 569–575. https://doi.org/10.1109/TPAMI.2009.187.
Scheibe, K., S. Nilakanta, and C. Ragsdale. 2017. Decision support system for optimized equipment turnover.. Washington, DC: Transportation Research Board.
Sun, Y., Z. Jiang, J. Gu, M. Zhou, Y. Li, and L. Zhang. 2018. “Analyzing high speed rail passengers’ train choices based on new online booking data in China.” Transp. Res. Part C: Emerging Technol. 97 (Dec): 96–113. https://doi.org/10.1016/j.trc.2018.10.015.
Van Hulse, J., T. M. Khoshgoftaar, and A. Napolitano. 2007. “Experimental perspectives on learning from imbalanced data.” In Proc., 24th Int. Conf. on Machine Learning, 935–943. New York: Association for Computing Machinery.
Wyrick, D. A., and S. Erquicia. 2008. Fleet asset life cycle costing with intelligent vehicles. Minneapolis: Univ. of Minnesota Center for Transportation Studies.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 2 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Feb 11, 2020
Accepted: Sep 17, 2020
Published online: Dec 9, 2020
Published in print: Feb 1, 2021
Discussion open until: May 9, 2021
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.