Exploring the joint impacts of income, car ownership, and built environment on daily activity patterns: a cluster analysis of trip chains
Bibliographic record
Abstract
Clustering activity patterns and identifying homogeneous travel behaviour through trip chain sequences offer valuable insight for transportation planners and policymakers in addressing transport equity problems and travel demand management. This study explores how income and car-ownership levels determine mobility patterns and travellers' decisions. Unlike previous studies that investigated the travel mode and destinations separately, we designed a novel, aggregated form considering the trip purpose and associated transport mode use as a unit of our analysis. To mitigate the subjectivity of rule-based approaches for trip chain analysis, we employ a novel sequence clustering framework to extract homogeneous clusters of activity patterns. Our results reveal that income and car-ownership levels influence travellers' travel decisions and mobility patterns. Among low-income carless households, 37% of their daily trips include care activities where women more frequently than men play this traditional role in a household by either public transit or a car as a passenger. In the low-income car-owner subsample, females still use public transit for their work trips, whereas males more often use the available car to commute to work. Males of wealthy carless households integrate public transit and active transportation for their daily trips when they live in high-density and more accessible neighbourhoods. While our findings demonstrate the impact of car ownership, income, and built environment on trip-chaining behaviour, we recognise that achieving transport equity will require tailored transportation and land use policies and investments that address the specific needs and barriers faced by different household types, particularly the most vulnerable ones in terms of sociodemographic characteristics, accessibility levels, and affordability issues. Hence, we recommend that policymakers and planners take a more holistic approach to transportation planning that considers the interplay of these factors to ensure that transportation systems and services are accessible, affordable, and equitable for all.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".