Ontario Tech team working on optimizing snow plow and waste collection routes in Oshawa

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Published March 20, 2023 at 9:22 am

With angry homeowners assaulting snow plow operators around the GTA for doing nothing more than their job the timing couldn’t be better for a student team from Ontario Tech University and the AVEC Research Lab to work on a program to optimize Oshawa’s snow removal routes.

The team, led by Dr. Jaho Seo, an Assistant Professor at the school’s Department of Automotive and Mechatronics Engineering, has been working with City operations staff to develop algorithms and mathematical optimization techniques, such as topology, to find ways to maximize snow clearing efficiency and safety while also saving on operational factors such as travel time and fuel consumption.

The study is also looking at ways to improve the efficiency of waste collection and street cleaning routes.

Major snow storms, like the one that dumped as much as 30 centimetres on Oshawa and most of southern Ontario earlier this month, require a massive mobilization of resources from municipal governments, which devote substantial planning and budgeting expenses to snow removal.

The job of clearing the streets as quickly and efficiently as possible is never quick enough for some frustrated residents, with one man in Oshawa charged with assault after trying to drag an operator out of his vehicle for leaving a ‘windrow’ at the end of his driveway, so it’s to everyone’s benefit to develop an effective snow plowing strategy.

That’s where Seo and his student team at the university’s AVEC Research Lab come in. His team worked on a snow plow optimization project for the Municipality of Clarington during the early stages of the pandemic in 2020 and 2021 and have applied what they learned to two concurrent projects – one funded by the City of Oshawa and Teaching City (a partnership of four post-secondary institutions that work together to develop sustainable solutions to urban challenges) and a second funded by MITACS Canada (a national research organization) to come up with optimized routes for snow plowing, street cleaning and waste collection.

The focus on the snow plow route optimization is residential streets, where space constraints such as parked cars and urban landscape features often make the job more difficult and consequently more time consuming.

“Obviously in heavy snow, all vehicles need to move slower than normal, including the plows themselves,” Seo explained.in a statement released last year. “It stands to reason that there must be a best-possible approach in terms of the number of available vehicles, and optimal routes that minimize such simultaneous considerations as turning directions, depot locations and street dead ends.”

Dr. Jaho Seo

Seo and his team use Geographic Information System studies and a couple of different algorithms (Dijkstra’s algorithm and a ‘tabu search’ algorithm) to compile data on the shortest possible paths.

“The method for approaching the routes in snow and ice maintenance is unique to each city, province, and country due to different operational constraints,” he said. “High-priority routes such as major highways and thoroughfares must be serviced first. Mathematical optimization techniques can be a tool that enables the design of efficient plowing operations.”

The hope is the current studies will validate some of the theories developed during the Clarington study. “Once the real-time data is evaluated against the 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, potentially leading to cost savings for taxpayers.”

Seo also noted they can also apply the knowledge from the snow plow study to other municipal services like salting, waste collection and street sweeping.

Tyler Parsons, who graduated from Ontario Tech with a degree in Mechanical Engineering in 2021 and immediately began working on his masters degree with Seo, said dividing the city into ‘clusters’ for waste collection can have a major influence on the workload distribution.

He admitted optimizing and automating curbside waste collection administration can be a “challenging problem” today  considering the continuous housing development within urban centers, but noted the clustering techniques can create a nearly 90 per cent improvement from the current arrangement.

The work can be “tedious” to do manually with potentially thousands of collection points, but using a weighted clustering arrangement capable of properly balancing the workload distribution for the waste collection team across each day of the week can lead to huge improvements in efficiency, Parsons said.

“The daily assignment of collection areas was a problem specific to the City of Oshawa (but) other cities that use a similar method to  collect curbside waste can benefit from using the proposed  workload balancing techniques. The proposed method will be especially useful for developing cities in creating collection areas and routes that optimize workload distribution.”

The MITACS-funded project is expected to be wrapped up by the end of the month while the City of Oshawa/Teaching City multi-year project should be finished by May.

To watch a video of the project work, visit AVEC Research Lab

With files from Bryan Oliver at Ontario Tech University

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