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.
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.
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.
(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 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.
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.
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.
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.
Tackling climate change is a complex task, one that depends on transformation of different sectors. Maritime shipping is the transmission belt of the global economy and continues to account for the majority of imports and exports. It is recognized as an energy-efficient mode of transportation compared to road and air transport. Yet, maritime transport has increased by 250% over the past 40 years, resulting in the sector contributing to 3% of total annual man-made greenhouse gas (GHG) emissions.
The goal of the project is to research and develop computer vision algorithms, software, and specialized hardware for the analysis of mixed traffic at intersections. Road users will be detected and classified as motor vehicles, pedestrians and bicycles. Road users will be geo-located within a 3D model of the intersection, tracked and classified according to trajectory. Our partner TransPlan will benefit in that they will be the Canadian receptor for the algorithms and software that Shahab generates.