Open AccessDashboards for Real-time Monitoring of Winter Operations Activities and After-action Assessment
Author(s) -
Jairaj Desai,
Jijo K. Mathew,
Woo-Sung Kim,
Mingmin Liu,
Howell Li,
Jeffrey D. Brooks,
Darcy M. Bullock
Publication year - 2020
Language(s) - English
Resource type - Reports
DOI - 10.5703/1288284317252
Subject(s) - snow , winter storm , revenue , weather station , environmental science , data assimilation , precipitation , meteorology , storm , scheduling (production processes) , resource (disambiguation) , transport engineering , computer science , operations research , geography , engineering , operations management , business , computer network , accounting
The Indiana Department of Transportation (INDOT) operates a fleet of nearly 1100 snowplows and spends up to $60M annually on snow removal and de-icing as part of their winter operation maintenance activities. Systematically allocating resources and optimizing material application rates can potentially save revenue that can be reallocated for other roadway maintenance operations. Modern snowplows are beginning to be equipped with a variety of Mobile Road Weather Information Sensors (MARWIS) which can provide a host of analytical data characterizing on-the-ground conditions during periods of wintry precipitation. Traffic speeds fused with road conditions and precipitation data from weather stations provide a uniquely detailed look at the progression of a winter event and the performance of the fleet. This research uses a combination of traffic speeds, MARWIS and North American Land Data Assimilation System (NLDAS) data to develop real-time dashboards characterizing the impact of precipitation and pavement surface temperature on mobility. Twenty heavy snow events were identified for the state of Indiana from November 2018 through April 2019. Two particular instances, that impacted 182 miles and 231 miles of interstate at their peaks occurred in January and March, respectively, and were used as a case study for this paper. The dashboards proposed in this paper may prove to be particularly useful for agencies in tracking fleet activity through a winter storm, helping in resource allocation and scheduling and forecasting resource needs.