Skip to main content

Smart snowplough research

Researchers at the University of Minnesota are working on a method that helps snowploughs determine exactly where slippery patches are and to target those specific areas with their sand-and-salt mixtures.
February 2, 2012 Read time: 2 mins
The sensor system that measures friction is attached to a wheel near the front axle of the plough
Researchers at the 584 University of Minnesota are working on a method that helps snowploughs determine exactly where slippery patches are and to target those specific areas with their sand-and-salt mixtures.

Based on measuring friction coefficients, a sensor system is attached to a wheel near the front axle of the snowplough, and when the sensor filters out vibration ‘noise’ and detects a loss of friction, it sends a signal to the sand-spreading equipment. A quarter of a second later, about the time it takes the applicator to arrive at the ice, the sand starts to be applied.

This automated system yields several benefits, according to researcher Rajesh Rajamani, a professor in the university’s Department of Mechanical Engineering who helped develop the technology along with colleagues Lee Alexander and Gurkan Erdogan.

For one, it will be helpful to know portions of road that tend to get slippery, and by using GPS technology, the 2103 Minnesota Department of Transportation (MnDOT), which is funding the research, could create a database of problem areas.

This smarter snowplough also stands to save a lot of sand and salt. Estimates suggest that Minnesota uses more than 200lb of sand and salt per person each winter, according to Alexander. “It’s just as important to know when to turn the sand off,” he says.

Related Content

  • July 24, 2012
    Cold efficiency
    Tools to support operational decisions in winter maintenance can remove subjectivity and increase efficiency; Vaisala's Danny Johns talks about latest developments Even the presence of trees at the roadside can have an effect on temperature An effective Road Weather Information System (RWIS) network can save a local road authority or jurisdiction tens of thousands of dollars or Euros'-worth of labour and consumables in a single night. Get those winter maintenance operations right over just three or four nig
  • May 7, 2020
    Columbia brings the noise to VRUs
    ‘Twalking’ – the practice of staring at a smartphone screen while walking – may be a matter for wry amusement for the non-addicted, but is potentially hazardous to the phone users. A US research project may have found a solution, finds Alan Dron
  • February 15, 2013
    Estimating winter road recovery time with traffic data
    In Minnesota, US, the most common measure for snow management performance is the time it takes to completely clear a roadway after a snow event ends. Currently, the Minnesota Department of Transportation (MnDOT) relies on visual inspections by its field crews to estimate this bare pavement recovery time. To help MnDOT more accurately and reliably estimate the performance of its snow management activities, researchers from the University of Minnesota Duluth (UMD) have developed a prototype process that uses
  • September 6, 2017
    Options abound for road weather sensing
    Meteorological organisations invest millions in super-computers to crunch data for ever-more accurate forecasts but inherent unpredictability means that other methods of alerting drivers and road authorities to fast-changing weather and highway conditions are essential. For years, static weather sensors to measure factors such as surface water, ice or high roadway temperatures have been embedded in highways to provide such data. But that is changing.