Relevant Degree Programs
Affiliations to Research Centres, Institutes & Clusters
Complete these steps before you reach out to a faculty member!
- Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
- Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Admission Information & Requirements" - "Prepare Application" - "Supervision" or on the program website.
- Identify specific faculty members who are conducting research in your specific area of interest.
- Establish that your research interests align with the faculty member’s research interests.
- Read up on the faculty members in the program and the research being conducted in the department.
- Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.
- Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
- Do not send non-specific, mass emails to everyone in the department hoping for a match.
- Address the faculty members by name. Your contact should be genuine rather than generic.
- Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
- Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to pique someone’s interest.
- Demonstrate that you are familiar with their research:
- Convey the specific ways you are a good fit for the program.
- Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
- Be enthusiastic, but don’t overdo it.
G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.
Graduate Student Supervision
Master's Student Supervision (2010 - 2020)
No abstract available.
With an increasing focus on bicycling as a mode of urban transportation, there is a pressing need for advanced tools for bicycle travel analysis and modeling. The objective of this thesis is to introduce “Biking schedules” to represent archetypal urban cycling dynamics along with its methods of construction and potential applications.Biking schedules are constructed by appending short trip segments, called microtrips, together. Three different methods of constructing biking schedules with both speed and road grade attributes are developed. As an initial proof-of-concept, the methods are applied and compared using a pre-existing demonstration data set of 55 hours of 1-Hz on-road GPS data from three cyclists. Biking schedules are evaluated based on their ability to represent the speed dynamics, power output, and breathing rates of a calibration data set and then validated for different riders. The impact of using coarser 3, 5, and 10 second GPS logging intervals on the accuracy of the schedules is also evaluated. Results indicate that the best biking schedule construction method depends on the volume and resolution of the calibration data set. Overall, biking schedules can successfully represent most of the assessed characteristics of cycling dynamics in the calibration data set within 5%. As a second step, the biking schedule construction methods are further developed and validated by collecting and applying a large, naturalistic, GPS-based data set of 2314 bicycle trips in Vancouver, Canada. We specifically explored the optimal microtrip definition to be adopted in constructing biking schedules. The choice of the optimal microtrip definition depends on the parameter that biking schedules are originally generated to model. Generally, the 150m microtrips generated the most precise biking schedules. The collected data are also used to compare the travel characteristics and construct biking schedules for regular and electric bikes. Results show that electric bikes travel 7 km/hr and accelerate 0.17 km/hr/sec faster than regular bicycles. Moreover, the total energy used to move electric bikes is almost twice as much as the energy used by regular bikes. These results have implications in designing bike lanes and safety analysis. Potential applications for biking schedules are also discussed.
Road grade is a major factor influencing cyclist physiology and travel decisions. Research studying cycling and other non-motorized transportation modes often use coarse elevation data sources to obtain the necessary grade information. In addition, routing applications such as Google Maps, Strava and RideWithGPS append the GPS data collected with elevation data from the coarse elevation datasets which can be inaccurate and inadequate. The objective of this research is to determine the best methods of obtaining road grade information on a network scale for bicycle travel analysis and to understand the limitations of the coarse data sources. Multiple elevation data sources, high resolution and coarse, are collected for the city of Vancouver, BC Canada. Different road grade estimation algorithms are then applied to the data sources at eight locations in the city where ground truth elevation data were surveyed using a total station. Different cycling performance measures were used to compare the elevation and road grade estimates of the locations to identify the data sources that accurately represent the true ground elevation for cycling analysis. Finally, the elevated structures in the City of Vancouver are characterized to help infer grade information in the absence of high resolution data sources.Results show that elevation data collected from Light Detection and Ranging (LiDAR) are the most accurate for elevated and non-elevated roads with mean absolute error in the elevation not exceeding 0.6 meters. Additionally, road grades derived from LiDAR data sources were closest to measured grade data. In the absence of LiDAR, coarse data sources can provide adequate grade estimates for cycling analysis on non-elevated structures. However, on elevated structures, especially ones without a single dominant grade, coarse datasets can only provide estimates of total elevation change or mean grade. Overall, the results show that it is vital to understand the accuracy and limitations of elevation data sources used in analysis and modeling of active travel.
Urban cyclist’s physical characteristics are important for advanced modelling of bicycle speed and energy expenditure, with applications including infrastructure design, network analysis, and health and safety assessments. However, representative values for diverse urban travellers have not been established. This study investigates the physical characteristics of real-world urban cyclists, including rolling and drag resistance parameters, and bicycle and cargo masses. Relationships among physical characteristics socio-demographics and travel behaviour are also analysed, and a bicycle cruising speed model is derived to illustrate usefulness of the sought parameters.Firstly, a 12-sensor, 100-meter coast-down test setup is developed and indoor and outdoor validation tests are performed. Outdoor validation tests generate rolling resistance coefficient estimates of 0.0064 ±0.0013 and effective frontal area estimates of 0.63 ±0.11 m².Secondly, resistance parameters were measured utilizing the novel coast-down test for 557 intercepted cyclists in Vancouver, Canada.. The average (standard deviation) of coefficient of rolling resistance (??), effective frontal area (????), bicycle plus cargo mass, and bicycle-only mass were 0.0077 (0.0036), 0.559 (0.170) m², 18.3 (4.1) kg, and 13.7 (3.3) kg, respectively. The range of measured values is wider and higher than suggested in the literature.Thirdly, the sample of intercepted cyclists is categorised based on observed physical attributes of the bicycle and rider. Three typologies defined through cluster analysis were identified as Road (R), Hybrid (H) and Mountain (M) style urban cyclists. The analysis indicates that cycling efficiency, perceptions, preferences, and habits are related to physical typology in a complex but consistent manner. M, H, and R cyclists are, in that order, increasingly more efficient, more comfortable in mixed traffic, moreIIconsistently year-round cyclists, self-reportedly faster, and engage in more physical activity. Physical typologies might help unveil new motivations in active travel behaviour and encourage urban cycling by a wider range of people.Finally, a mathematical framework is derived from first principles to determine speed from cyclist characteristics (power output, gearing, resistance parameters) and roadway attributes. Application of the speed estimation framework to the problem of traffic signal clearance interval timing illustrates the utility for probabilistic, context- sensitive roadway design.