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Decision Support System for Winter Highway Maintenance Management

  • Author / Creator
    Li, Yipeng
  • Snow accumulation on roads is a major safety concern during winter. Winter road maintenance is an effective approach to maintain roads in good driving conditions and reduce accidents. A common approach for a winter road maintenance project is to partition the road network into multiple service areas, set one depot in each area, and assign a truck fleet to each depot to clean the roads during snow events. To conduct operations effectively, proper planning in both the long-term and short-term is essential. For planning a project in the long-term, the appropriate fleet size for each depot needs to be determined, taking into account operations under different snow scenarios. For short-term, lookahead project planning, the operation routes, labor working hours, and the required fleet size for the upcoming snow event need to be determined. For urban areas, roads usually have similar weather conditions as they are close to each other. Thus, the required vehicle fleet sizes are similar between different snow events, and vehicles can follow similar routes every time. However, for maintenance operations on highways, regional weather events must be considered due to the large spatial scale of the highway network. Different snow events can have different impact areas, which can affect the required fleet size and optimal operation routes. Moreover, the exact impact area of a snow event is difficult to forecast and monitor because of the limitations in weather data. Weather observations and forecasts are usually specific to a few locations that have weather stations, so it can be hard to determine the weather situation on a road between weather stations with differing weather conditions. Therefore, a decision support system is needed to assist project planning for winter highway maintenance operations using limited weather data and considering the stochastic nature of weather events. In this thesis, two simulation models were developed to help in planning winter highway maintenance operations. The first simulation model uses a performance-based approach to help determine the fleet size in the long-term. This model uses road network information, historical weather data, and vehicle speed distribution as inputs. Monte Carlo method is used to sample random snow areas, and the performance of a certain truck fleet is evaluated by calculating its operation time-cost under various snow scenarios. An appropriate fleet size can then be selected based on an acceptable confidence level. The second simulation model is developed for short-term lookahead project planning. Weather forecast and road network information are used as inputs, and the model can provide short-term fleet size forecast, operation schedule, and route suggestions based on the input data. This model can also be updated in near real-time and can generate updated results based on the operation progress using weather observations and vehicle tracking data. Interpolation methods were also used to estimate the detailed weather condition on each road using limited weather data.

  • Subjects / Keywords
  • Graduation date
    Fall 2020
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-g7bz-5j53
  • License
    Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.