Municipalities in Ontario devote a large portion of their planning and budgetary expenditures to snow removal. A blizzard like the one that hit Durham Region on January 17 with up to 55 centimeters of snow, or even a more typical light snowfall, requires a massive mobilization of resources to clear roads and sidewalks and allow people , goods and services to move safely again.
Delays in snow removal, even slight ones, can cause ice to form and make a bad situation worse. Storm drains should be unobstructed, fire hydrants and other utility equipment should be readily available. An effective snow removal strategy is therefore a crucial issue for the economy.
Dr. Jaho Seo, researcher at Ontario Tech University applies technology to help communities find ways to maximize snow removal efficiency and safety while saving on operational factors such as travel time and fuel consumption.
“Obviously, in heavy snow, all vehicles have to move slower than normal, including the snowplows themselves,” says Dr. Seo, an assistant professor at Ontario Tech Faculty of Engineering and Applied Science. “It stands to reason that there must be a best possible approach in terms of the number of vehicles available and optimal routes that minimize concurrent considerations such as turn directions, depot locations and dead ends. We can also apply this knowledge to other municipal services such as salting, waste collection and street sweeping.
Dr. Seo has recently worked on Geographic Information System (GIS) optimization studies and techniques with the Municipality of Clarington (snow removal) and the City of Oshawa (trash collection). A third study on noise filtering algorithms for mobile machinery in industrial sectors facing dusty environments is also underway with the Korea Institute of Machinery and Materials. These results could also apply to adverse weather conditions (snow, rain, fog, etc.).
“The method of approaching roads for snow and ice maintenance is unique to each city, province and country due to different operational constraints,” says Dr. Seo. “High priority routes such as highways and major arterials must be serviced first. Mathematical optimization techniques can be a tool for designing efficient tillage operations.
Dr. Seo’s main objective is to optimize snow removal routes in residential areas.
Do the math
Here is the math part. “Topology” is a field of mathematics that examines how physical spaces are organized and structured for position, and how these spaces are connected. Dr. Seo generated an initial network topology using GIS data from an open road map database. Then he applied two algorithms (known among experts as Dijkstra’s algorithm and the “taboo search” algorithm) to compile data over the shortest possible paths. The two algorithms have a different role in the optimization process.
“These algorithms generate the optimal route and shortest travel time, minimizing or eliminating unwanted turn directions and maximizing safety by considering a hierarchy of services. Our first study being limited to simulation results, we plan to validate the proposed method by field tests taking into account real-time dynamic routing situations and on-site weather conditions.
Once real-time data is compared to simulations, municipalities will know the best routes each vehicle should take. This information will help communities determine the ideal formula for available resources and budgets, which could lead to cost savings for residential ratepayers.
Dr. Seo’s lab assistants at Ontario Tech include Master of Applied Science students Abdullah Rasul (now a graduate), Tyler Parsons and Ali Afzalaghaeinaeini.
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