Data-Driven Simulation Approach for Short-Term Planning of Winter Highway Maintenance Operations

Winter highway maintenance operations are performed to ensure safe driving conditions during snow events. However, variability in truck speeds and changing weather conditions limit the ability of practitioners to optimize plans in a timely manner. The time required to manually adjust plans in response to actual conditions prevents modifications from being completed and applied during the operation phase. To overcome this challenge, a data-driven, near real-time simulation approach to assist short-term planning of winter highway maintenance operations is proposed. The approach integrates dynamic project data to quickly (1) predict required truck fleet size for upcoming operations, (2) devise operation schedules, and (3) recommend operation routes. Functionality and validity of the proposed approach was demonstrated using both an illustrative example and a real case study. The proposed approach was found capable of rapidly generating operation plans that were more efficient than current practice.

• Record URL:
  https://doi.org/10.1061/(ASCE)CP.1943-5487.0000980
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/08873801
• Supplemental Notes:
  • © 2021 American Society of Civil Engineers.
• Authors:
  • Li, Yipeng
  • 0000-0003-4140-7088
  • RazaviAlavi, SeyedReza
  • 0000-0002-7372-0354
  • AbouRizk, Simaan
  • 0000-0002-4788-9121
• Publication Date: 2021-9

Language

• English

Media Info

• Media Type: Web
• Pagination: 04021013
• Serial:
  • Journal of Computing in Civil Engineering
  • Volume: 35
  • Issue Number: 5
  • Publisher: American Society of Civil Engineers
  • ISSN: 0887-3801

Subject/Index Terms

• TRT Terms: Highway maintenance; Maintenance management; Short range planning; Simulation; Winter maintenance
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01789747
• Record Type: Publication
• Files: TRIS, ASCE
• Created Date: Nov 30 2021 10:23AM