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Investigating the Use of Anonymous Cellular Data for Intercity Travel Patterns in Alberta Open Access


Other title
intercity travel
passive data
cell phone data
Type of item
Degree grantor
University of Alberta
Author or creator
Hui, Tin Ying
Supervisor and department
Kim, Amy (Civil and Environmental Engineering)
Examining committee member and department
Qiu, Tony (Civil and Environmental Engineering)
Shirgaokar, Manish (Earth and Atmospheric Science)
Kim, Amy (Civil and Environmental Engineering)
Department of Civil and Environmental Engineering
Transportation Engineering
Date accepted
Graduation date
2017-11:Fall 2017
Master of Science
Degree level
Intercity trips or long-distance trips have been understudied and overlooked by researchers and public agencies in comparison to routine trips that are relatively shorter and often within an urban area. Currently, intercity travel demand is increasing, and accounts for a significant portion of total mileage travelled. This increasing demand also leads to increasing issues such as congestion, energy consumption, and emissions. Governments are recognizing the need to understand current intercity travel patterns for infrastructure investments and environmental policies, and data such as origin-destination (OD) flows and intercity demand are valuable for strategic planning of the highway networks. However, traditional methods of data collection to estimate OD demands and/or flows through household or roadside surveys are time consuming and expensive, and public agencies have historically prioritized survey data collection within their jurisdiction, typically urban boundaries. An emerging (albeit imperfect) alternative is passive data sources, such as anonymous cellular data. Anonymous cellular data can provide large random samples with reduced bias and provide results much faster and at much lower cost than travel surveys. It also has a low deployment cost, as it does not require any additional equipment installation or measuring devices. The purpose of this thesis is to investigate how anonymous cellular data may be used to extract more information and features about intercity travel patterns. In this research, two days of anonymous cellular data for the province of Alberta, Canada are used to extract intercity trips and infer trip modes used. Intercity trips were first extracted between the two major cities - Edmonton and Calgary. The extracted data show that most trips take between 2 – 3 hrs, as well as a smaller portion that take between 0 – 1 hrs. This shows that anonymous cellular data provides a reasonable reflection of the two modes, as a direct drive trip between the two cities take approximately 3 hours, and a flight takes 45 minutes from takeoff to landing. This analysis was expanded to all intercity trips between cities and urban areas in Alberta. Intercity trips between fourteen urban zones in Alberta (including urban service area Fort McMurray and oil sands camps Fort MacKay) were extracted using a similar methodology. Overall, the data shows that larger cities have more trips originate and destined there, and the distance between cities also affected the share of trips (i.e. smaller cities had fewer trips but the highest proportion of trips to and from cities nearby, sparsely located cities had very few trips anywhere). Two methods were utilized to infer the trip mode for trips between Edmonton Calgary, first by categorizing the travel times using upper and lower limits, second by hierarchical clustering of the travel times. Hierarchical clustering of trips less than 8 hours results in distinct clusters that represent air trips, ground trips, and longer ground trips (likely made with stops). The clusters showed a mode split that ranged from 12 – 25% air trips and 88 – 75% ground trips for the two days. Hierarchical clustering was then conducted for all intercity trip pairs that had direct air service between them, with a unitless, rescaled travel times based on the average ground travel time for each pair. Mode splits ranged widely between the two days of data, which could be due to seasonal variations in trip patterns (i.e. more people flying in winter than in the summer) or sampling issues in the data. The mode split results shows the trend that cities further apart will have a higher share of air trips then ground. This work contributes to the existing research on intercity travel and passive data applications in transportation. It builds on existing research that have identified origin-destination flows and shows how trip mode can be inferred from travel times, using clustering techniques. This research is limited by the small sample size of two individual days of data, and the data contains only a sample of all records from the cellular service provider. Though the data here is limited, it demonstrates its ability to provide useful information about intercity travel behaviour. Traditional data sources and passive data both have their own limitations, but if used together, they can overcome current limitations in data.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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