Description
This video shows the complete volumetric based method using 1 truck for three consecutive calibration procedures. Three calibrations for the truck took 22 minutes making this process quicker than legacy methods.
The information provided in this resource is based on the intelligent snowplow database for Indiana managed by the Joint Transportation Research Program (JTRP). The database itself is not available for download in PURR because it is both prohibitively large and continually updated, but this resource is part of a series of tools based on those data.
For fleet management and training, Automatic Vehicle Location devices were installed on all 1,100 Indiana Department of Transportation (INDOT) fleet trucks beginning in October 2020. The resulting intelligent snowplow database stores real-time vehicle location, salt spreader controller and vehicle data, and dash images every minute, allowing agencies to actively monitor and deploy their assets. INDOT manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide deployment and operations. Discovery of calibration metrics partnered with fleetwide telematics enabled the development of analytic dashboards that allowed real-time evaluations and adjustments to be made during winter operation activities. These tools will allow the agency to better treat and enhance safety for road users.
Cite this work
Researchers should cite this work as follows:
- Mahlberg, J.; Jewison, M.; Bell, J.; Bullock, D. M. (2021). Intelligent Snow Plow: Calibration Workshop Training Time-lapse. Purdue University Research Repository. doi:10.4231/XBET-8A88