Construction zones are one of the leading contributors to Toronto’s ever-growing congestion. The aim of this study is to develop an integrated construction zone traffic management framework to minimize disruption of the traffic and reduce the effect in terms of congestion. This study leverages historical and real data collected from on-board construction trucks provided by the partner organization to find an insight as to how far upstream and downstream of the work zone congestion propagates.
PledgX is interested in building a solution that aims to optimize the bidding process to maximize key performance indicators for contactors and vendors. For bidding optimization, several strategies and methods have been proposed; however, with the massive amount of available bidding datasets, the quality and performance of such methods are questionable. Machine learning introduces intelligent solutions to optimize the bidding decision, however these solutions are applicable to a range of prediction or classification tasks.
(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.
The focus of this project is on building performance evaluation (BPE) in residential houses in Southern Ontario. Eight green homes will undergo BPE to see how well they are performing. The project will compare current building performance to the designed building performance. This comparison can help to see whether a “performance gap” exists. A performance gap is a difference between the actual building performance and the designed building performance. It will use on-site testing, collection of existing data, and observations from residents of the homes.
The objective of the study is to develop an algorithm to streamline and automate the decision making process for implementing the municipal wastewater collection flushing program. Traditionally, a municipal flushing program, or pipe cleaning, is based on a time-cycle approach. This means that all sewer pipes in the network are treated the same, ignoring variables, such as the pipe physical attributes, site conditions, use and service are ignored. The driving paradigm for this project is to switch from a quantity-focused practice toward a quality-focused approach.
The objective of this research is to develop models to assess potential benefits of cloud-based Smart Dual Fuel Switching System (SDFSS) of the residential hybrid HVAC system of electric air source heat pump (ASHP) and natural gas furnace/boiler (NGFB) for simultaneous reduction of energy cost and greenhouse gas (GHG) emission.
Higher drug discovery failure rate has led to an increase in drug prises in the market. BioInteract technique is designed to combat the failure rate by identifying most potential therapeutic drug for a broad range of genetic diseases by analysing the drug effect at molecular level. It is a scoring system that can rank drugs based on their ability to restore the key interactors at the molecular level in the mutant cell and thus predict how successful a drug will be in the given disease.
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.
Currently, the scientific community is aware of the potential of dye sensitized solar cells - they are translucent, conduct 100% renewable energy using the Sun’s energy, and are inexpensive to manufac-ture. They possess the potential to revolutionize Canada’s energy system for the better. This research project will show, using a unique solar simulator, how dye sensitized solar cells can work efficiently under more conditions than have currently been tested: such as air pollution, position of the Sun rela-tive to the Earth, and elevation.