Lifecycle Assessment Using Snowplow Trucks’ Automatic Vehicle Location Data

Snowplow trucks serve a crucial role in winter maintenance activities by removing, loading and disposing snow. An effective performance monitoring and analysis process can assist transportation agencies in effectively managing the snowplow trucks and maintaining normal functioning of roadways. Previous literature suggests that most snowplow truck performance analysis is done through cost-benefit analysis at the macro-level to determine the optimal life cycle for the entire truck fleet. However, the proposed optimal life cycle could lead to waste of resources and may incur bias due to the ignorance of performance variations resulting from endogenous and exogenous features. More importantly, it fails to identify the contributable factors to performance deterioration. With the proliferation of data in recent years, the aforementioned concerns can be addressed through predictive machine learning techniques in a data-driven fashion. In this study, the authors apply machine learning techniques, including the random forest (RF) algorithm and a support vector machine (SVM) to predict the performance of snowplow trucks. Using the snowplow truck fleet managed by the Utah Department of Transportation (UDOT), both models are implemented and it is demonstrated that RF outperforms linear SVM with regard to prediction accuracy. Further, a feature importance analysis can assist transportation agencies to improve truck replacement strategy by identifying crucial factors for their performance. Lastly, a sample application of the developed prediction model suggests the threshold of work intensity for preventing rapid deterioration of trucks’ performance under various working environments.

  • Record URL:
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  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    University of Utah, Salt Lake City

    Department of Civil and Environmental Engineering
    Salt Lake City, UT  United States 

    Mountain-Plains Consortium

    North Dakota State University
    Fargo, ND  United States  58108

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Liu, Xiaoyue Cathy
    • Yi, Zhiyan
  • Publication Date: 2021-3


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Maps; References; Tables;
  • Pagination: 41p

Subject/Index Terms

Filing Info

  • Accession Number: 01769335
  • Record Type: Publication
  • Report/Paper Numbers: MPC-21-429
  • Contract Numbers: MPC-544
  • Created Date: Apr 12 2021 9:22AM