Lifecycle Assessment Using Snowplow Trucks’ Automatic Vehicle Location Data

Details

• Creators:
  Liu, Xiaoyue Cathy ; Yi, Zhiyan
• Corporate Creators:
  University of Utah
• Corporate Contributors:
  Mountain-Plains Consortium ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; North Dakota State University
• Subject/TRT Terms:
  Algorithms Asset Management Automatic Vehicle Location Life Cycle Analysis Machine Learning Snowplows Winter Maintenance
• Publication/ Report Number:
  MPC-544
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  Mountain-Plains Consortium
• Abstract:
  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, we 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.
• Format:
  PDF
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha-512:8f104cf496771380af9a6800f461f9ff989bd4cd6459ea69652476f2d00baf665e6c9f2760b33a8695f889e29ba73751953c2a243c1bb990763e9e9c8eaa8170
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/56630/dot_56630_DS1.pdf
• File Type:
  [PDF - 2.07 MB ]