SUMMARY The Lateral Ignition and Flame Spread Test (LIFT) is used to characterize fire ignition and flame spread on solid materials. This test requires the operator to visually monitor the flame spread over a combustible material and manually record the position of the flame during an experiment. Visual inspection limits the quantity of data obtained from a test and introduces uncertainty in the measurement. In this study, we use narrow-spectrum light with a peak wavelength of 450 nm and a digital camera with frequency-matched optical filters to capture images of surface charring, which underlies the flaming combustion, in a LIFT apparatus. The imaging technique reduced unwanted energy emissions from the flame in the visible light spectrum, allowing the test operator to directly view the charring of the material; which is otherwise hidden behind the flames. We describe data processing routines to analyze the sequences of high-resolution images. The method improves temporal and spatial resolution of the surface charring compared to visual observations.

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This research examines the current state of the airport landside operations using microsimulation models to help understand how these areas will change with the introduction of connected and automated vehicles. Data collection from an existing North American commuter airport curbside was conducted to support this research. The curbside models provide comparisons of capacity and estimates of level of delay to travellers based on the different uses of space for the curbside. The analysis of the travellers' journey is explored further by following their path through the pedestrian access corridor between the curbside and the airport security. Data of pedestrian movement speeds and total travel time within the corridor were collected. This was modelled to understand the impact of dynamic changes to desired walking speeds. The combination of the collected data and models gives a complete overview of the airport travellers' journey between the curbside and airport security.iii Acknowledgements This research would not be possible without the support and help of individuals and organizations who provided necessary support and guidance.Thank you to the airport authority for providing access to the data allowing this thesis to be completed using real-world data. Without this access this thesis would not have the same practical scope.Thank you to members of both Carleton and York University for help with both the discussion behind the permission of the data acquisition and the collection itself. A special mention to Timothy Young for his many long trips between the two Universities during this process. That you to the scholarship providers who helped fund this research. Thank you to NSERC and OGS for providing external scholarships. Thank you to the Department of Civil and Environmental Engineering at Carleton University for providing internal scholarships and teaching assistant positions. Thank you to NSERC for the internship opportunity of the Summers of 2021 and 2022 through the TrustCAV program provided to me through the Principal Investigator Prof. F.

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