Visualization Tools that Integrate Automated Vehicle Location (AVL) Data and On-board Device Monitoring Equipment Can Help Snowplow Supervisors Make Timely Decisions and Improve Winter Maintenance Operations.
Iowa DOT Study Demonstrated Data Management and Visualization Tool for Snowplow Operations.
Made Public Date
11/24/2021

1253

Iowa,
United States
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Identifier
2021-01065

Winter Operations Decision Support Tools for the Iowa DOT Maintenance Bureau

Background

The Iowa DOT Maintenance Bureau manages nearly 900 snowplows, which are continuously transmitting important operational data during winter operations. These data provide truck locations using automated vehicle location (AVL) pings, and report operational status such as whether the plow is up or down, and which materials, if any, are being applied to the roadway. This study explored new ways to support timely and accurate decision making during winter operations based on the stream of data transmitted by the snowplows. The two key objectives of the project were to (1) demonstrate opportunities to visualize and aggregate winter operations data to support timely and effective decision making for supervisors and key decision makers using data from two winter seasons (2017-2018 and 2018-2019), and (2) investigate the performance of nine different snowplow blade types in terms of cost. A software tool was developed which provides map, tabular, and graphical information specific to when and where winter operations materials were applied. The tool demonstrates one day of data with the integration of snowplow AVL data with material application information by time and location. The tool also shows the spatial and temporal relationships among these multiple factors during winter conditions.

Lessons Learned

The following are lessons learned on the processing and integration of AVL data for the snowplow fleet.

  • Integrate large data streams to a common reference and create visualizations to display information available for decision making. Integration helps process the data stream within one minute so that supervisors can make timely decisions during a winter storm. Visualization of this data stream, such graphics depicting snowplow miles driven, number of passes made for a road segment, and material applied along with compliance to application rates by truck can help supervisors.
  • Process the data stream coming from snowplows periodically to conduct material quantity calibration using field measurements. It is important to schedule site visits during fall, winter and spring months to record odometer readings, record blade measurement, and conduct blade state/position calibration using field and corresponding data obtained from snowplows. In addition, mileage and blade-wear data from snowplows should be presented via a web interface tool to allow monitoring and identification of possible issues. 
  • Understand there may be AVL data inaccuracies during the first winter season. Physical front blade measurements are often assumed accurate but, often, no differences in measurements might be reported for long periods of time. This may be related to various factors, such as the limitations in the measurement technique (wear not being easily detectable at 1/16 inch) and inaccuracies in measurement reporting.
  • Use plow status data and blade measurements together to assess blade wear. This study demonstrated that blade measurements were relatively frequent, but the plow status did not appear reliable. Therefore, a combination of reliable plow status readings and the higher frequency of blade measurements proved to be necessary to yield blade-wear rates.
System Engineering Elements