2021-2022 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
Karim Habib – University of Alberta
Sanjana Hossain – University of Toronto
Felita Ong – University of Toronto
Mingjian Wu – University of Alberta
Cassidy Zrobek – University of Manitoba (unable to accept)

Usman Ahmed – University of Toronto

Transport Canada Scholarship $6,000

Karim Habib – University of Alberta

Mainly, my research focuses on investigating traffic safety from pure human factors perspective. This is vital for the advancement of the knowledge in traffic safety because 95% of collisions occur due to human error. My mission is to design forgiving and welcoming roads that absorb different human capabilities to improve safety and save lives. In this respect, in the remaining time of my research, I am working on assessing the workload demand (complexity) of the urban and rural settings. This helps developing design calibrations, safety models, and improved planning to protect car drivers and vulnerable road users. This assessment happens by combining psychological measures to serve the urban and rural design to accommodate different drivers’ workload supply.

My work is closely related to automation, sustainability, and equity. From automation perspective, human factors are undoubtedly necessary in situations where a transition from automation to manual driving occurs due to manual requests or system failure. Also, to address sustainability, human factors represent a cornerstone for Vision Zero plans across North America. As Vision Zero aims at eliminating fatal and serious collisions on roads, human factors play a crucial role regarding creating designs that do not overwhelm drivers and eliminate errors. From equity perspective, human factors address the differences between different age groups which may serve aging populations needs.

To summarize, the development of geometric design assessment tools that consider the complexity of different design elements is necessary to accommodate different drivers and vulnerable road users in different situations whether in manual/automated cars or young or older drivers. Finally, I would like to acknowledge that I am a strong advocate for Vision Zero and my dream and goal is providing safe, accessible, equitable, and gender inclusive transportation system that ensures no life loss under any circumstance.

Transport Canada Scholarship $6,000

Sanjana Hossain – University of Toronto

My research deals with developing fusion methods to combine travel data from multiple sources for accurate travel behaviour and demand analysis. The research is important in today’s context because of two main reasons. First, household travel surveys are facing issues related to incomplete sample frames, low response rates, short time and space coverage, and trip underreporting. On the other hand, emerging passive data sources have high spatial and temporal resolution, but they lack socio-demographics of the people. Thus, there is a trade-off of information among the different data sources and fusing them is essential to obtain improved representation of passenger movement. Second, the increasing market penetration of new mobility options such as ride-hailing and mobility-as-a-service is making it imperative to have clear understanding of the trip characteristics and how they are affecting the travel behaviour of people. Such in-depth analysis requires detailed data which cannot be obtained from a single source.

As such, my research project aims to utilize data fusion techniques to: (1) address trip underreporting of emerging travel modes in surveys for improved behaviour analysis, (2) generate high-resolution data for dynamic planning models, (3) enrich a source with additional data fields for better passenger travel representation, and (4) improve short- and long-term forecasts of travel demand by providing improved input data. Specifically, it focuses on fusing ride-hailing GPS trajectories with travel survey and land use data to analyze why riders use these services. The inferred trip purpose pattern extends our understanding about the travel demand generated by the services. Another important part of the research is to fuse transit smart card transactions with survey data, land use information, and network characteristics to construct high-resolution and up-to-date origin-destination matrices of transit trips which can serve as input for a public transport planning analysis tool.

The research also focuses on developing methods to merge information from multiple travel surveys so as to facilitate detailed analysis of travel behaviour and provide robust forecasts of travel demand. Overall, my research will produce comprehensive and accurate input data for improved evidence-based planning of land use and transportation alternatives.

Transport Canada Scholarship $6,000

Felita Ong – University of Toronto

Evaluating the Competition Between Ride-Hailing and Public Transit
The proliferation of ride-hailing services provided by Transportation Network Companies (TNCs) such as Uber and Lyft in Canada has raised a growing concern that these services will attract customers away from public transit services. The competition between TNCs and public transit is of particular concern to transit agencies, since they often depend on farebox revenues to fund transit operations. In Metro Vancouver, approximately 57% of operating costs are funded through fares [1]. Given that public transit services are often publicly funded, there is a need to ensure that the demand for these services is not taken over by TNC services such that public transit can remain affordable and continue its pivotal role in providing equitable access to people of various socio-economic backgrounds, including those without cars or with disabilities.

Studies have shown that there is indeed a relationship between TNCs and public transit, although the nature of this relationship tends to be context-dependent. Factors such as public transit service level and type play a significant role in determining the effects of ride-hailing services on transit ridership. For example, TNCs were found to complement public transit when they are used during times of infrequent or non-operational transit service [2]. TNC services have also been found to complement rail services and substitute for bus services [3]. It should be noted, however, that these studies were conducted in US cities, where transit is likely a less competitive mode than in major Canadian cities. Furthermore, in Metro Vancouver, commercial TNC services were only introduced in early 2020 [4], and therefore the demand relationships between novel ride-hailing services, public transit, and other urban modes have not been explored in the region.

This research aims to investigate the relationship between the services provided by TNCs and public transit in Metro Vancouver based on a stated preference survey administered to residents of the region. While the geographical context of this study is Metro Vancouver, the proposed research will help develop policy guidelines for TNC operations that will facilitate a more complementary, rather than competitive, relationship with transit services in different regions across Canada.

[1]      TransLink, “2019 Business Plan Operating and Capital Budget Summary,” 2019. [Online]. Available: https://www.translink.ca/-/media/translink/documents/about-translink/corporate-reports/2019_business_plan_operating_and_capital_budget_summary.pdf.
[2]      Shared-Use Mobility Center, “Shared Mobility and the Transformation of Public Transit,” Chicago, 2016.
[3]      Y. Babar and G. Burtch, “Examining the Heterogenous Impact of Ride-Hailing Services on Public Transit Use,” Informations Syst. Res., vol. 31, no. 3, pp. 820–834, 2020, doi: 10.1287/isre.2019.0917.
[4]      S. Boynton, “Uber, Lyft launch in Vancouver,” Global News, 2020. https://globalnews.ca/news/6455682/uber-vancouver-friday/.

Transport Canada Scholarship $6,000

Mingjian Wu – University of Alberta

The main purpose of my research is to develop an assisting system for winter road maintenance (WRM) operations via advanced techniques including deep learning and geostatistics. During the winter season, vast areas in North America suffer from frequent snow, sleet, ice, and frost events. Such adverse weather events could lead to dangerous driving conditions with consequential effects on road safety and mobility. Thus, it is paramount for roadway administrators and transportation agencies to acquire real-time or near-future road surface conditions (RSC) information to make more informed decisions on their various WRM activities (e.g., salting and plowing). For this reason, as well as to reduce the large cost of WRM, road weather information systems (RWIS) have gained attention for their capability of providing real-time RSC information, and have become more widely used over the last decade amongst highway authorities.

While RWIS stations are widely adopted to monitor vast road networks, they can only be located at select areas due to budgetary constraints. It is therefore indispensable to fill those large spatial gaps that exist between stationary RWIS stations to promote safer driving conditions and lower WRM activities cost. Furthermore, most RWIS nowadays are equipped with cameras that provide users with a direct view of the road conditions being covered; however, checking the road conditions via these cameras is still being done manually, which hinders the full utilization of these rich image-based road condition data for optimizing maintenance services and improving the travel of the general public.

For these reasons, my research has four specific objectives as follows:
1) Synthesize knowledge on characterization and spatial variations of road weather and surface conditions, and methodologies and models for continuous mapping of such;
2) Prepare and process event-based RWIS datasets and others (e.g., weather, traffic, camera images, digital elevation models, road geometry, etc.) that are required for both spatial mapping and image recognition;
3) Test and improve the existing image recognition models using the new training/testing data;
4) Implement a prototype web-based application for showcasing the developed spatial mapping and image recognition solution, and demonstrate the application with real world usage scenarios.

CN Scholarship $6,000

Usman Ahmed – University of Toronto

Freight transportation plays a critical role in the economic development of an urban area. Freight vehicles are a source of emissions, noise pollution, pavement damage and safety implications. Parking is another problem faced by delivery vehicles, typically in dense urban areas. These problems affect the overall livability of an urban area. Therefore, efficient urban freight models are required to understand the commercial vehicle movements and to support public policymaking.

My research is focused on developing an urban freight transportation model for the Greater Toronto and Hamilton Area (GTHA) which is one of the fastest-growing areas in Canada. The urban freight transportation model focus on key logistics decisions made by firms in the GTHA region. Three critical logistics decisions are considered which are the choice of carrier type, vehicle type, and shipment size. The choice of carrier type focuses on whether a firm outsources or uses firm-owned vehicles for shipment transportation. The choice of vehicle type focuses on the commercial vehicle used for shipment transportation. It includes commercial vehicles such as cars, pickup/cube vans, single-unit trucks, and tractor-trailers. The shipment size choice model focuses on the size of shipment in terms of the weight of shipment selected to be delivered. These decisions could be made independently or jointly by firms. Therefore, my research focuses on developing models that could represent independent and joint decisions of the three choices.

An important component of the modelling framework is a tour-based model. Since most of the freight trips are in the form of tours, it is important to model freight tours instead of trips. As part of my research, I am also developing a tour-based model which assigns shipments on a transportation network in the form of tours. The proposed model will also distribute shipments to different regions. The application of machine learning algorithms in developing some of the models described above is also the focus of my research