Electrifying Household Car Fleets: Dynamics of Electric Vehicle Adoption and Travel Behavior in the Transition to Electric Mobility
Bibliographic record
Abstract
The transportation sector contributes around one quarter of global greenhouse gas emissions, making the transition from internal combustion engine vehicles (ICEVs) to electric vehicles (EVs), particularly battery electric vehicles (BEVs), essential for achieving climate goals. This thesis examines the electrification of household car fleets, focusing on the dynamics of EV ownership and travel behavior during the transition to electric mobility, and generates societal, environmental, and policy insights. It advances existing research on EV adoption by exploring three key dimensions: the linkage between EV adoption and household fleet composition, the timing of adoption, and the integration of EVs within household fleets. It also investigates how EVs are used compared with conventional vehicles. Using data from the Dutch National Travel Survey, an original survey, and a literature review, the thesis applies behavioral, spatial, and statistical modeling to identify the determinants of EV adoption, adoption timing, fleet integration strategies, and usage patterns. The findings reveal distinct patterns across household types and ownership structures, showing how social influence, policy awareness, and perceptions of EV benefits shape both the decision and the timing to adopt. Analysis of vehicle usage further uncovers differences in travel behavior between EV and conventional vehicle users, emphasizing the roles of ownership status and charging accessibility. Overall, this thesis deepens understanding of household-level transitions toward electrified mobility. It contributes to innovation and adoption theory and provides practical policy insights for promoting BEV ownership and accelerating private fleet electrification, supporting a more sustainable and inclusive transport future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".