Demand Forecasting in Sustainable Transportation

Sustainable Transport Services (STS) project is a community-focused initiative aimed at providing an alternative means of road transportation that is environmentally and economically friendly across Atlantic Canada. The project is aimed at reducing the carbon footprints in our operating community, while providing affordable taxis services to students, newcomers, and other residents.

Improving Structural Integrity of Buried Pipelines under Ground Deformation using Special Geobuffer Blocks: Field Testing and Strain Monitoring

Steel pipelines as key Canadian infrastructure are often exposed to various geological and environmental conditions that cause defects such as cracks, dents, and gouges in the pipe wall, which can lead to pipe fracture in pipelines, compromising the entire pipeline's functionality and resulting in significant economic losses, environmental issues or even fatalities. There are several methods to predict the fracture capacity, including experimental testing, analytical approaches, and advanced numerical methods.

Analysing Marine Vessel effects on whales in Boundary Pass in the Salish Sea, British Columbia

Boundary Pass is a busy vessel traffic area of the Salish Sea and is a critical foraging habitat for many cetacean species, specifically Southern Resident killer whales, transient (Bigg’s) and humpback whales. With the shipping lanes passing through this area, whales are at risk of disturbance from underwater noise and, at times, from close vessel proximity. However, DFO has created a seasonal vessel no-go zone (Interim Sanctuary Zone) for whales to reduce acoustic and physical harassment in the region.

Complex network based data analysis for shared mobility

This project aims to build a new analysis toolkit for shared bikes, escooters, and cars. The method is based on a mathematical theory called complex network. Like internet and human brains, transportation system possesses network structure. To study its topological properties, we can calculate some indices that encode the information of the network. There are theoretical and empirical studies proposing and examining various models based on spatial networks structure of transportation, and especially shared mobility.

Hydrology and geochemical cycling of a complex urban stormwater system

The hydrologic cycle in major cities is influenced by roadways, rooftops, and other features, resulting in greater runoff and poorer water quality. At airport sites across Canada, de-icing compounds can also degrade water quality by altering water chemistry in surface ponds as well as subsurface water (i.e., groundwater). This project aims to characterize water movement between surface stormwater ponds and groundwater at the Calgary International Airport, to better understand the physical connections between these key parts of the hydrologic cycle.

Regional Risk Assessment of Underground Metro Infrastructure under Different Dynamic Loads

Underground metro infrastructures are subjected to different types of dynamic loads that would hinder their ability to function properly. This proposed research aims to assess the risks of city-scale underground tunnel networks under both periodic human-induced vibrations (i.e., blasting and drilling vibrations) and short-term extreme earthquake hazards. We will develop a GIS model for the tunnel soil/rock profiles in Montreal by synthesizing a comprehensive dataset for tunnel designs, embedment depths, and soil/rock properties from geotechnical surveys.

A Predictive Cluster-based Machine Learning Pricing Model

Dynamic pricing models create price by assessing total cost, demand, and timing to customize the price to the moment. The models enable both buyers and sellers to settle a price that is very custom to their specific needs. Bison Transport Inc. has a network model that monitors profit and a pricing engine that monitors margin. The network model needs to evolve in critical ways to facilitate dynamic pricing. The current model allows viewing of the network from a variety of vantage points- region, customer, driver, asset, service type and time (day of week, time of day, season of year).

Parking Occupancy Inference With LiDAR Sensors

Parking is a cumbersome part of auto travel in urban areas, primarily due to lack of information on the location of available spots. Sensors can be deployed to detect occupancy, but they often fail due to their high costs and detection inefficiencies in outdoor spaces. This project pursues a feasibility study of using LIDAR sensors, which overcome some deficiencies, for parking detection. LIDARs have a wide field of view, are robust to outdoor disturbances, and can be provided at cost given the recent advancements in the autonomous vehicle industry.

Predicting Hydrological Impacts on Remote Infrastructure Using Satellite Imagery and Machine Learning

After 17 months of closure due to flooding and washouts, Arctic Gateway Group (AGG) took over operation of Hudson Bay Railroad (HBR) in September 2018 and reopened 29 washouts in 54 days. Servicing northern First Nations communities and the Port of Churchill, one of the most important aspects of the HBR is safety. As a result, water monitoring and management remains a critical priority for the company. In particular, the section of rail line known as the ‘Herchimer’ remains an isolated and difficult portion of the track to monitor.

Digitization of supply chain routing decisions for freight transportation

Identifying an optimal routing, that ensures a delivery passage between suppliers and vendors, with a minimum cost while respecting the various constraints (including shortest delivery, availability of fleets and routes, traffic, etc.), could be a challenging task. This is mainly due to the need to solve a combinatorial optimization problem with discrete choices of pathways and routes that could become an NP-hard problem. Researchers and practitioners have adopted heuristic approaches to simplify the solution process.