2022-2023 Scholarship Recipients

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

2022-2023

Transport Canada
Asif Anik – Dalhousie University
Ahmed Foda – McMaster University
Muntahith Orvin, University of British Columbia
Cassidy Zrobek – University of Manitoba


Transport Canada Scholarship $6,000

Asif Anik – Dalhousie University

Mobility disruptive events have significant long-lasting social, economic and travel-related impacts. To illustrate, the current effects of the COVID-19 pandemic may trigger changes in travel related decision-making, such as, telecommuting, e-shopping, vehicle ownership or residential location choice of households. It is necessary to understand these complex reactions and develop sector-wide policies where transport authorities learn to prepare for and effectively manage such extreme events. This includes building up potential and resources to identify sustainable plans and policies that will enable a community to return to ‘post-crisis normal’ safely and efficiently. For this, the policy makers need to have a clear understanding of the impacts of the crisis events on transport and land-use systems.

Integrated transport and land-use models (ITLMs) are large-scale modelling systems that simulate population’s mobility decisions to predict the transformation of urban environment. ITLMs are well-suited for modeling travel and land-use dynamics because they are able to capture changing human behavior within the land-use transformation processes. However, the existing ITLMs have not yet been extended to model disruptive events, such as pandemics, natural disasters or introduction of automated vehicles. Considering the gaps in the literature, my research proposes to advance methods, techniques and tools to enhance the capabilities of conventional ITLMs. The technical objectives are as follows: 1) to extend an integrated urban systems model by coupling short-term and long-term decision processes; 2) to accommodate uncertainty and risks for predictive modelling of extreme events; 3) to analyze alternative mobility-scenarios for assessing the impacts of the events on transport and land-use systems.

My research will focus on modelling disruptions and risks in the traditional ITLMs to predict people’s travel behaviour under uncertainty. Multi-sourced data (e.g., travel survey, mobility reports) will be used to develop advanced statistical and econometric models, as well as machine learning models, and incorporate them within the extended ITLM. An agent-based micro-simulation modelling platform will be developed using C# programming language in Microsoft Visual Studio to longitudinally simulate individuals’ travel-related choices. Developed methods and tools can be used to carry out scenario modelling and simulation of individuals’ travel behavior accounting for the effects of the mobility disruptive events. Travel behaviour of households is complex and hard to predict, and it is even more difficult to forecast the behaviour of people amidst or following a crisis. My research will provide a platform to analyze and predict how the transport and land-use environment may look like in the future through anticipating changes in mobility trends of households. Therefore, it will assist to bolster resilience in case of any future disasters, provide guidance in preparedness measures and transport infrastructure strengthening. The research will also provide a unique opportunity to reconfigure post-crisis transport policy and use the crisis as a way to steer transport and environment systems in a more sustainable direction. The outcomes of my research will be highly beneficial for governments, policy makers and transportation modelling research community.


Transport Canada Scholarship $6,000

Ahmed Foda – McMaster University

Global warming is one of the most pressing challenges for our cities. Various international commitments (e.g., the Paris agreement and the COP) are actively working on mitigating global warming. In the transit sector, Battery-electric buses (BEBs) are considered a superior tool to significantly reduce the soaring rates of transportation greenhouse gas (GHG) emissions.
However, BEB’s supporting infrastructure requires sophisticated design, planning, and optimization before phasing out fossil-fuelled buses. This sophistication is attributed to the numerous conflicting decisions.

My research aims at developing a generalizable toolkit for optimizing BEB systems configuration, which addresses the intra-dependency between the system components over the lifespan of the service.
In particular, the model will include all the BEB system parameters as decision variables, such as charging station configuration, fleet configuration, and charging schedule. This will address the required trade-offs (e.g., competing objectives). In addition, the charging infrastructure’s spatiotemporal optimization (e.g., sizing and allocation) are integral to the model.

The proposed model is based on our novel accurate trip-level energy consumption estimation algorithm that considers the transit system operation uncertainties. Moreover, the BEB system configuration will utilize an optimal charging strategy, including partial and continuous charging, considering the temporal well-to-tank GHG emissions factors and electricity time-of-use tariffs.
Integrating the BEB system with the utility grid has impacts on both systems. Therefore, the model will consider the systemic resiliency considering BEB inter-dependency with other critical infrastructure systems (e.g., utility-transit nexus). As such, utility constraints and the probability of failure (e.g., electricity outage and equipment malfunction) will be included in the proposed model to enhance the BEB system’s robustness and resiliency.

The developed model will provide the decision-makers with the optimal BEB system design components, upfront costs, annual operational costs, and system total GHG emissions in a flexible GUI to promote transit electrification. The developed model will be disseminated in a user-friendly interface for all stakeholders.


Transport Canada Scholarship $6,000

Muntahith Orvin, University of British Columbia

This research focuses on developing advanced econometric and machine learning-based methods to improve the behavioral representation of residential relocation and microsimulate within an agent-based integrated urban modeling (IUM) system. IUMs are large-scale models for urban regions, which simulate peoples’ location and travel choices, and their interactions to predict travel demand and land use patterns. Residential relocation is a critical component of IUM because decision of where to live interacts with vehicle ownership, mode choice, travel distance, and consequently impact on carbon footprint. To capture the relocation behavior and improve prediction accuracy of integrated models, we need to develop advanced behavioral modeling techniques. Residential relocation is a long-term decision process. Accurate representation of relocation behavior is challenging due to change in behavior for unprecedented events such as COVID-19. For instance, increased demand for larger dwelling in suburban areas due to telecommuting and reduced need to commute during pandemic. This is responsible for increased price of single-detached houses. Many of the changes in behaviors might have a longer-term consequence and might not be reversible over the next decade. Therefore, IUM presents a fitting platform to test the impacts of post-pandemic policies on land use and transportation systems. However, to do that, it is important to represent the changes in relocation behavior during and after pandemic within IUM.

This study conceptualizes residential relocation as a four-stage decision process where households first decide to move, then search for locations, assess price and finally move to a location. The objectives of this study are three-fold: i) develop advanced econometric and machine learning-based methods, ii) test the applicability of IUM considering the impacts of COVID-19 for predicting housing price, and iii) deploy the behavioral models within a new generation IUM. To adequately represent these decision-making behavioral dynamics, advanced methodologies such as hazard-based duration, discrete choice, and machine learning models will be developed. For example, for mobility decision, hazard-based modeling technique will be deployed to accommodate the duration of stay dynamics at a residence. To consistently translate behavior into simulation platform, IUM will adopt a hybrid of event-based continuous time and discrete time microsimulation technique. For example, microsimulation will involve the simulation of decision to move as a continuous time event-based decision followed by simulation of location choice as a discrete decision. The applicability of IUM in response to COVID-19 will be tested in the context of housing market. Specifically, separate price models for pre- and during-pandemic will be implemented within IUM. IUM will be deployed, calibrated and validated for 100% population of Central Okanagan region of British Columbia for 2011-2021. Since the model starts simulation from pre-pandemic to pandemic period, this facilitates the opportunity to test the applicability of this IUMs for unprecedented socio-economic shocks. This testing will be performed in the context of housing price, since Canadian housing market has been significantly impacted by this pandemic. This research will contribute by improving the behavioral representation of residential relocation decisions within IUMs and enhancing the capacity of IUMs to incorporate the effects of unprecedented socio-economic shocks.

 


Transport Canada Scholarship $6,000

Cassidy Zrobek – University of Manitoba

Government agencies invest in traffic monitoring programs with the goal of quantifying the volume, type, and weight of vehicles using the roads within their jurisdiction, as well as how these usage characteristics change with time. Given that it is not feasible to directly collect vehicle classification data all the time on every road, agencies have traditionally used a combination of continuous and short-term counts to estimate the quantity and composition of traffic on their roads. Recently, alternative approaches for estimating traffic volumes, such as using ubiquitous vehicle GPS probe data, have been developed to allow for volume estimation at a network-wide scale.

The purpose of my research is to investigate the following options for enhancing system-wide truck volume estimates in the Canadian Prairies: 1) the use of new non-intrusive short-term count equipment that can classify vehicles into 13 classes, and 2) the use of emerging third-party data products. For the first option, the research will use data from Manitoba to address questions about what short-term count duration should be considered when deploying new technologies with improved classification capabilities. While several studies have assessed the impact of short-term count duration on annual estimates of total traffic, few have focused on the effect of count duration on estimates of truck traffic and individual truck classes. For the second option, Manitoba truck volume estimates obtained using traditional methods will be compared to those from StreetLight, which is a third-party data provider. StreetLight uses machine learning algorithms to produce traffic estimates from commercial vehicle GPS data, location-based services data, census data, and weather data. Currently, no publicly available comparisons of this nature have been conducted in Canada.

This field of study is important because the road freight transportation system is essential to the economy of the Canadian Prairies. In addition, high-quality and geographically representative truck data is needed for many applications, including pavement design, bridge design, highway maintenance, road safety assessments, road capacity analysis, vehicle emission estimations, and freight planning.