Types of travel patterns and contributions to urban transport emissions
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School of Engineering |
Master's thesis
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Authors
Date
2024-09-11
Department
Major/Subject
Sustainable Urban Mobility Transitions
Mcode
Degree programme
Master's Programme in Urban Mobility
Language
en
Pages
149
Series
Abstract
Given Sweden’s ambitious national-level goals to reduce greenhouse gas emissions from transportation, various policies must be designed and implemented in many different municipalities. To do this effectively, it is helpful to understand the various travel patterns that contribute most to everyday passenger transport emissions in these places. This thesis contributes to this understanding by developing two typologies of travel behaviour from app-based, GPS-tracked travel diary survey data. Via k-means cluster analysis, the trips, trip chains, activity spaces, and mode choices of travellers in Umeå, Sweden were segmented into 6 weekday clusters and 7 weekend clusters, interpreted with the help of original visual tools. Via HBEFA emissions factors, the transport emissions of travellers were computed and assigned to their clusters, and the weighted emissions contribution of each cluster was assessed. To verify the cross-contextual validity of the typologies, random forest classifiers were used to extract clusters in both Skellefteå and Gävle, Sweden, revealing similar segments with similar relative emissions contributions. Although segments in Umeå were less emissive in absolute terms and larger sections of their population belonged to relatively sustainable behaviour segments. Of the 6 weekday segments, Busy Car-Oriented Workers, Non-Workers, and On-the-Job Travellers contributed the majority of weekday passenger transport emissions and a minority of the population in each municipality. With the exception of Skellefteå, where they constituted 52% of the population. Of the 7 weekend segments, All-Round Car Drivers, Otherers, All-Round Car Passengers, and Busy Visitors contributed the majority of weekend emissions and a minority of the population in each location. The most emissive overlaps between weekday/weekend segments were also identified. Additionally, 11-17% of each population, despite relatively sustainable weekday behaviour, produced emissions at overall rates that may be considered unsustainable when measured against 2030 national emissions targets. In all, these typologies may be used to target, inform, and justify policy interventions aimed at reducing transport emissions at the municipal level.Description
Supervisor
Mladenovic, MilosThesis advisor
Sharmeen, FariyaClark, Anna
Keywords
travel behaviour segmentation, transport emissions, k-means, cluster interpretation, trip chaining, cluster analysis