Open Collections

Lum, Spencer

University of British Columbia

This thesis presents a methodology for identifying highly impacted locations on the provincial highway system of British Columbia (BC), Canada, due to disruption events caused by five groups of natural disasters, and for visualizing the degree of impact of these disruptions. Data is obtained from the province’s DriveBC road condition and incident information system. A data parsing procedure is developed to improve data governance. Based on the data provided by DriveBC, natural disaster disruption events occurring on the BC highway network for the years 2017 through 2021 were identified and categorized into the five groups of natural disasters. The impact caused by each disruption event was represented by a score calculated using a weighted linear sum multi-criteria decision analysis (MCDA) model, which uses four criteria to produce an impact score for each event based on a pairwise comparison between each pair of criteria. The Kernel Density Estimation (KDE) for Lines method enables the visualization of the degree of impact of natural disaster disruption events by estimating the density of events weighted by the impact scores of all events found within an array of 50-km by 50-km raster cells. Higher resulting total impact scores are linked to higher impacted highway locations. Highway locations most impacted by events in the five groups were identified and mapped. A top ten list is provided for each map. Connections between the top highway locations and extreme weather events are made when possible.

Text; Still Image

2024-04-24

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/87987

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/