Effects of off-road vehicle activities on plant and soil properties in Canadian ecosystems

The use of off-road vehicles (ORVs) in natural environments has accelerated dramatically over the past few decades, increasing the potential for ecosystem degradation and the need to establish policies and develop technologies that minimize the impacts of ORVs on the environment. Although the environmental consequences from ORVs are known to be highly degrading and a threat to ecosystem integrity and natural functioning, research on the physical and environmental impacts caused by different ORV activities remains limited, specifically those aimed at exploring low-impact technologies.

Infrastructure Corrosion Assessment Magnetic Method (iCAMM) technology for macro and micro defect detection of rail track

Because of the usefulness of non-destructive testing in the assessment of different types of materials, they have attracted widespread interest in the last years. Non-destructive testing (NDT) is a descriptive term used for the examination of materials and components in such a way that allows materials to be examined without changing or destroying their function. NDT plays a crucial role in everyday life and is necessary to assure safety and reliability. For instance, they are widely used in detecting defects in steel rebar in reinforced concrete.

Eco-Friendly Styrofoam Substitute for Sustainable Food Packaging

Food packaging, particularly packaging for fish, relies heavily on the use of Styrofoam (i.e. expanded polystyrene or EPS). The world’s concern for environmental sustainability has prompted a need for eco-friendly alternatives and has led governments to enact single use plastics bans in many areas, such as Montreal and New York.

Freight consolidation and container loading optimization

This project investigates a complex freight consolidation and loading problem faced by Frontline Carrier Systems Inc., a leading transportation and supply chain provider in North America. Frontline seeks to optimize their transportation costs by minimizing the number of trucks required to ship a set of customer orders within a finite planning horizon.

Stochastic Optimization Approach for the Multi Depot Vehicle Scheduling Problem

Vehicle scheduling is one of the main planning problems for transit agencies. While it is relatively simple to solve in the deterministic and single-depot setting, these assumptions are unrealistic in real-world applications. Specifically, ignoring major sources of uncertainty (such as travel times) and making decisions over average predictions can lead to inferior schedules that incur additional costs and reduce the quality of service during execution. In this project, we consider the multi-depot vehicle scheduling problem under uncertainty.

Advancing TTC Ridership Analytics and Revenue Forecasting Tools for Improved Transit Planning

(TTC) for improved public transit planning and better transit service delivery. With the implementation of PRESTO Card, TTC now generates real-time data on how often and where transit riders interact with the TTC’s infrastructure and network. PRESTO Card data allows new ways to capture transit demand in real-time and makes it possible to deploy state-of-the-art data science and predictive analytics to develop ridership forecasts for varying time horizons. The ridership forecasts could then be used to generate forecasts for farebox revenue.

Examining the operation of Public Pools and Spas in the COVID-19 Era

The COVID-19 pandemic affected every aspect of our lives, and recreational water facilities were not immune to this with several questions and concerns about potential exposure to the virus at these facilities. This research project aims to understand experiences, needs, and attitudes towards the use of recreational water facilities, namely, public pools and spas during the COVID-19 pandemic.

Symbolic Model-Based Design of a Semi-Autonomous Vehicle Prototype Implementing Independent Wheel Torque Vectoring for Training an Advanced Driver Assistance System

There is a strong belief that autonomous vehicles will play a vital role in the future of the global transportation economy. There, however, exists many open challenges which need to be overcome to realize this future vision. One such challenge is the acceptance from the driver to relinquish full control of a vehicle and ultimately putting one’s safety in the hands of a computer.

Quality Assurance and Safety Tools for Emerging Drone Technologies

Drone Delivery Canada (DDC) designs and operates high performance Remotely Piloted Aerial Systems (RPAS) to deliver payloads between depots and warehouses. Safety and quality assurance (QA) play an integral role in assuring that drone technology is accepted by the public, consumers, and the Canadian government. The demand for more advanced testing on both the drone hardware and software must be thoroughly carried out to achieve such acceptance. The proposed research project focuses on meeting this demand by investigating both the physical safety and control system quality of the drone.

Vehicle Dynamics Modelling and Simulation for Use in the Development of a Self-Healing Auto Cyber Security System (SHACS) Proof-of-Concept

Vehicles rely on small computers located in various places. The electronic signals sent between these computers must be dependable. However, currently these signals can easily be hacked which threatens the vehicle and the people in and around it. A project is underway, involving Akimbo Technologies Inc., Solana Networks Inc., and the Carleton University Applied Dynamics Laboratory to develop methods for protecting vehicles from this threat.