2023-2024 Scholarship Recipients

CTRF wishes to thank the sponsors of the current scholarship program, without whom these awards would not be possible.
Thank you.

Transport Canada

Abdelmoneim El Naggar – Western University
Kashfia Nokshi – Dalhousie University
Hasan Shahrier – Dalhousie University
Elahe Sherafat – Toronto Metropolitan University

Transport Canada Scholarship $6,000

Abdelmoneim El Naggar
University of Western Ontario

Many regions around the world are experiencing rapid population growth at alarming rates, placing significant burdens on existing infrastructure. Indeed, by 2050, the number of people living in urban areas will reach about 2.5 billion (United Nations World Urbanization Prospects, 2018). The only viable solution to the resulting increased demand on the existing transportation systems and public services is underground transportation systems. Tunnelling induces settlement troughs that may result in severe or irreparable damage to nearby infrastructure, usually in heavily populated and urbanized areas. By considering the effect of tunnelling on existing infrastructure, researchers and engineers can develop methods to reduce the carbon footprint and negative impact of underground transportation systems on the environment and human health. Indeed, the link between developing means of reducing the impact of transportation on human health and the environment and the effect of tunnelling on existing infrastructure lies in the concept of sustainability.

My research project specializes in understanding and assessing the impact of such transportation systems and developing green sustainable capacity upgrade systems to help mitigate any effects on the surrounding environment. Effective anticipation of tunnelling effects limits the possibility of design oversight or damage, thereby resulting in a substantial reduction in restoration and maintenance efforts. Likewise, this research aims at laying out the foundation of design guidelines for design codes to improve tunnel design and assessment to ensure the safety of the occupants.

By minimizing the impact of tunnelling on existing infrastructure, engineers can ensure that underground transportation systems are sustainable and efficient, contributing to the overall goal of reducing the impact of transportation on human health and the environment, which would result in the development of a sustainable transportation system that minimizes negative effects and promotes overall well-being.

Transport Canada Scholarship $6,000

Kashfia Nokshi
Dalhousie University

Integrating Dynamic Traffic Assignment (DTA) Framework with Emission Models to Estimate Greenhouse Gas Emissions

Urbanization, population growth, as well as the rapid evolution of transportation networks require dynamic planning and operations. A wide range of models is essential to develop the transportation environment and its effects on society. The current models use aggregated and clustered data to satisfy data limitations and computational constraints. On this increasing transport demand, only disaggregated model at the street level can meet the efficiency for policy analysis. The dynamic behavior of individual vehicles along with the user can be simulated by using microsimulation for transportation and land use modeling practices. Though simulation-based Dynamic Traffic Assignment (DTA) models have received a lot of attention in recent years, their applications, particularly for large-scale networks, remain difficult and thus, scarce. Additionally, reducing greenhouse gas emissions from the transportation sector is a crucial step in safeguarding a sustainable environment.

My research focuses on the traveling effects on the transportation network and the reduction of environmental pollution caused by the transportation system of Canada specially Nova Scotia. The objectives of this study are to develop a dynamic traffic assignment along with emission modeling. And to utilize the output to run different policy recommendations based on the simulations.

Two agent-based software namely, TASHA and MATSim are used for dynamic traffic assignment framework as well as emission modeling. TASHA works on the classical travel demand forecast modeling. It is a microsimulation model and needs the transportation survey to improve the conventional four-step forecast models. At the same time, MATSim is designed to handle large networks. Then the output generated by the TASHA and MATSim models is utilized to calculate emissions on the network.

The framework will help Nova Scotia with Canada’s most challenging greenhouse gas reduction goals for 2030. Gradually it can help to achieve net zero by 2050. The country is shifting its fuel consumption to clean energy sources for reducing its carbon footprint. This study’s main target is a database and outputs that are more useful and realistic for implementing any strategy. This dynamic research will concentrate not only on emission reduction but also on the recommendation of new policies for a better future.

Transport Canada Scholarship $6,000

Hasan Shahrier
Dalhousie University

The transportation sector, a pioneer of modern civilization, plays a significant role in creating pollution. According to a report from the World Bank, the transportation industry accounts for over 64% of worldwide oil consumption and 23% of energy-related CO2 emissions. Replacing conventional vehicles that use an internal combustion engine (ICE) with electric vehicles (EV) is one method to reduce CO2 emissions from the transportation industry.

This research will conceptualize an integrated transport and energy modeling (ITEM) system that includes different scenarios of EV adoption and charging strategies to estimate finer-grained emissions. It also comprises the calibration and validation of ITEM system using the 2022 Halifax Travel Activity (HaliTRAC) survey data to prove its credibility while capturing the behavioral change and decision-making process of potential EV adopters. The major objectives of this research are, 1) develop micro models to explore the rate of EV adoption, and potential charging locations, following a random utility-based econometric modeling approach; 2) extend the agent-based microsimulation modeling structure developed in C#.NET programming language to simulate the travel related attributes longitudinally (year by year) for EV users, and 3) calculate GHG emissions for different scenarios of EV by considering both the Equilibre Multimodal Equilibrium (EMME/4) platform and Motor Vehicle Emission Simulator (MOVES) software.

This research incorporates individual’s emerging mobility behavior within the traditional activity-based travel demand modeling framework and provides opportunity to forecast long-term decisions, such as, purchase trends and level of charging facilities required for EVs. Moreover, the results from this research will benefit the development of local and regional policy interventions to promote the development of facilities for the EV industry. Through these interventions, government, planners, engineers, and researchers can ensure the development of equitable and robust sustainable transportation infrastructure, better accounting for a healthier environment to mitigate GHG emission.

Transport Canada Scholarship $6,000

Elahe Sherafat
Toronto Metropolitan University

Autonomous Robots and Human Interaction in Shared Urban Areas, Pose Prediction

The advances in Intelligent Transport Systems (ITS) are laying the groundwork for putting the concept of a smart city into practice. Autonomous vehicles (AVs) and robots (ARs) are essential components of smart cities. In recent decades, numerous research targeted AVs and their interaction with pedestrians and cyclists.

In contrast, despite the benefits of deployment of autonomous robots for delivery purposes in urban areas, including responding to the increased e-commerce and online shopping, reducing congestion and cost of delivery, and being environmentally friendly, few papers aimed at investigating their interaction with humans.

Despite the aforementioned advantages and the existence of various companies offering Autonomous delivery robots, their widespread usage remained in the pilot stage in most areas. The reason for that is the challenges and safety hazards that ARs could bring for pedestrians, especially for people with disabilities, visual impairment, children and elderly people and being distracted. In my thesis, we aim at human and robot interaction in the interest of safe, foresighted and socially acceptable path planning of robots in urban spaces shared with pedestrians. To achieve this goal, the robot must comprehend the human’s movement, behaviour, intention and future path. The first step for that would be human pose estimation and prediction. That means understanding of current and predicting the future joint locations of each pedestrian.

Initially, we deployed a SLAM-based dataset for the multi-person human pose estimation in the presence of a Segway robot using YOLOv7, a vision-deep neural network. The network demonstrated high performance in dealing with crowded indoor and outdoor scenarios. The next step would be deploying the memory neural networks to predict the future pose of humans using sequences of human poses obtained from the YOLOv7.

Besides, we plan to collect an enriched dataset for human-robot interaction using 1)deploying Visual Immersive Reality technology available in LiTrans, and 2)walking our own robots in Toronto Metropolitan University campus areas.

I would believe this research will result in an accurate prediction of the human pose while interacting with ARs, fills the gap that existed in the previous studies and eases further behavioural analysis of human and robot interaction. This, in turn, enables the widespread deployment of ARs in urban areas.