Application of Unsupervised Machine Learning to Increase Safety and Mobility on Roadways after Snowstorms

ABSTRACTThe impact of a snowstorm on the safety and mobility of roadway transportation depends mainly on the storm’s level of severity. Defining storms’ severity, though, is challenging due to the high number of weather parameters needed to describe these events and the non-linear relationships among these parameters. Finding patterns among snowstorms can conceivably simplify this process and help practitioners better analyze and prepare for such events, even when the severity is not explicitly quantified. Therefore, this study interrogated historical data to assess and compare clustering methods and to identify patterns manifesting in snowstorms to lay the necessary foundations for building a more reliable and objective winter severity index. The research team selected three hierarchical clustering methods that differentiated similar groups of snowstorms among more than 2,000 events dated between 2006–2016 in Nebraska. The team then evaluated the performance of these methods using the Calinski-Harabasz index. A range of clustering scenarios were reviewed visually using principal component analysis to determine the optimal number of clusters. The results indicate that while some districts can be described by as few as three clusters, others can experience up to six different clusters of snowstorms. The use of PCA and visualization in this context can facilitate a better understanding of these high-dimensional data, and the findings of this study can help agencies better comprehend snowstorms and prepare for them, which can help communities to maintain the safety and mobility of their drivers.

• Record URL:
  https://doi.org/10.1061/9780784482445.045
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482445
• Supplemental Notes:
  • © 2019 American Society of Civil Engineers 2019
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Gholizadeh, Pouya
  • Walker, Curtis L.
  • Anderson, Mark
  • Esmaeili, Behzad
• Conference:
  • ASCE International Conference on Computing in Civil Engineering 2019
  • Location: Atlanta Georgia, United States
  • Date: 2019-6-17 to 2019-6-19
• Publication Date: 2019

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 351-358
• Monograph Title: Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience

Subject/Index Terms

• TRT Terms: Cluster analysis; Highway safety; Machine learning; Mobility; Pattern recognition systems; Snowstorms; Statistical analysis; Visualization
• Uncontrolled Terms: Principal component analysis
• Geographic Terms: Nebraska
• Subject Areas: Data and Information Technology; Environment; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01708168
• Record Type: Publication
• ISBN: 9780784482445
• Files: TRIS, ASCE
• Created Date: Jun 21 2019 5:11PM