BluWave-ai
When: June, 2025
* Dates are subject to change
Where: Virtual demonstration.
Area: Internal snow plow routes
Project Partner:
- City of Vaughan • Transportation and Fleet Management Services – Operations and Maintenance
- Groovenauts, Inc.
Snowplow and Salting Prioritization
The City’s winter maintenance operations include salting, windrow clearing, plowing, and snow removal along more than 2,200 kilometres of roads and 1,200 kilometres of sidewalks. As soon as snow or ice accumulates, the City has protocols for maintenance schedules on roads and sidewalks. Vaughan currently prioritizes main roads which are travelled more frequently, have steep hills or sharp curves, or serve as connecting roads that provide access to most residential streets, and then residential roads. These timelines may be impacted by major snow events.
This prioritization of roads and timelines to respond may be impacted by factors, such as major snow events or increased on-street parking activities. Proactive analysis and real-time data can potentially improve response times and identify priority routes to manage the snowplow and salting teams more efficiently.
BluWave-ai's EV Fleet Orchestrator platform will partner with Groovenauts - a quantum computing company based in Japan - to apply their combined experience in optimization and fleet management. Their solution will use AI-powered tools to analyze Vaughan’s maintenance routes and traffic behaviours to make recommendations for the optimal salting, windrow clearing, plowing and snow removal operations and showcase how advanced tools for fleet management can potentially improve response times and reduce the duration for winter maintenance.
Purpose:
The demonstration aims to evaluate how recommendations from AI-powered management tools could improve the response times of snow maintenance and assess its data insights on logistical challenges of traffic networks for road users during weather events.
Expected Outcomes:
- Plowing and salting route recommendations maintenance operations
- Measure time to run the optimization model
- Assess how it could work with existing operations protocols
Company Description:
BluWave-ai is using Artificial intelligence (AI) to ease the transition to renewable energy and electric vehicles. Using AI enabled models to predict the generation of wind and solar energy and load in a grid we can optimize operation of energy storage systems, EVs, and other DER's to allow us to absorb more renewable energy and reduce demand on the grid. We can also apply this technology to optimizing other complex challenges.
AI-Powered Logistics Optimization
June 5, 2025: Presenting at the Driving Innovation: Mobility Technology 2nd Showcase Event
About Quantum Computing Innovations for Fleet Management
The EV Fleet Orchestrator is a software solution that is used to create models for logistical challenges – such as fleet management – and review predictive information for better management. Using AI-powered and quantum computing resources, the technology can both be input with more variables and can process results faster than a typical algorithm.
By rapidly consolidating these complex variables through their digital solution, the EV Fleet Orchestrator can analyze information more efficiently. It produces a complex model with optimized logistics of the problem based on the data input.
The optimization output considers variables such as energy, demand of resources, fleet routes, equipment schedules, and service needs.
No personal data will be collected in this demonstration. The demonstration will use historical data on winter maintenance operations – such as routes, resources used and fleet management information.
In this demonstration, BluWave-ai's solution could potentially optimize salting and snowplow operations by using AI to prioritize routes based on data-backed parameters such as weather, routes, resources, and road conditions. This AI-driven approach can enhance efficiency, reduce idle time, and improve response times for winter road management to allow the City to prioritize routes and clear roads faster.
This could allow for more effective planning and management of the snowplow fleets, and enhance the City’s roadways during the winter maintenance of snow events.
The data shared with BluWave-ai will be used for the fleet optimization model to inform the City on potential efficient or enhanced winter maintenance routes for snow removal and salting routes.