Optimizing Snow and Ice Route Removal Operations Using Vehicle Routing Problems and Geographic Information System

Abstract

Snow and ice route removal activities play an essential role in travelers’ safety and roadways’ reliability. They often require the deployment of a large number of snowplow trucks on a vast maintenance area and involve a set of vehicle routing problems with multiple objectives and constraints. Many transportation agencies have limited resources and budgets for snow and ice route removal activities during winter maintenance operations. The objective of this paper is to propose a constraint-based snowplow optimization model to accommodate vehicle routing problems and justify the optimized fleet size for achieving the maximum level of service in snow and ice route removal. The proposed snowplow optimization model was developed using the geographic information system (GIS) base maps created by using ArcGIS Pro (version 2.7). After the model was tested and verified by experts in the Kansas Department of Transportation, it was applied to the snow and ice route removal activities in a district in Kansas as an illustrative example. The optimization results showed that the level of service could be increased up to 81% with an average route removal efficiency of 86% and the total travel time required to treat all snow routes in the selected district can be reduced by approximately 29 h for one treating iteration. The proposed model contributed to the winter operation maintenance body of knowledge by providing an efficient approach to snow and ice route removal activities. The findings of this study also may assist transportation agencies in optimizing their snow route removal practice in winter maintenance operations. This study developed a snowplow route optimization model using a geographic information system (GIS)-based software platform to enhance the efficiency of snow removal of Departments of Transportation of states in their winter maintenance. The model outcomes provided a detailed set of optimized snowplow route maps with time-related parameters (e.g., total travel time, plowing time, and deadhead time), snowplow route efficiency, and percentage of level of service (LOS) satisfaction of snow removal. Then, transportation agencies can utilize the optimized routing networks to monitor their resources spent on snow and ice control activities and increase the LOS in their maintenance operations. The results of this study enable transportation agencies to effectively justify the fleet size required at each service area and efficiently allocate limited resources to maintain all roadways in an economic, safety, and reliability manner. This study also facilitates the automation of snowplow optimization processes using GIS to provide satisfactory services during snow and ice events. The proposed model can serve as a tool to assist the agencies in directing any future facility locations and capacities for their winter maintenance.