Identifying the recurring travel patterns among older people: A mobility motif analysis with smartphone track and trace data
Bibliographic record
Abstract
Understanding older people's mobility patterns and out-of-home activities is crucial for promoting their independence, active ageing and well-being. This study leverages mobility motif analysis to identify recurring travel patterns among older people and investigate how they are associated with out-of-home activity profiles. We collected continuous 12 weeks of mobility data using a smartphone Track and Trace app on 9147 journeys made by 43 participants (3612 person-days) aged 55 and above in the West Midlands Combined Authority, UK. To address self-selection and low participation rates, we matched participants with national survey data based on out-of-home activity profiles. Using network analysis, we identified distinct mobility motifs within daily travel networks. A total of 106 distinct mobility motifs were identified, with 19 key motifs representing the most recurring travel patterns, collectively accounting for 92.2 % of all daily travel networks. The distribution revealed high consistency with group profiles. Further analysis revealed inter-group variations in travel frequency, mode use, motif types, and journey purposes among the three groups. These findings provide evidence-based insights for developing age-friendly urban systems and targeted interventions to enhance mobility inclusivity among older populations. The identified motifs reveal diversity in older people's travel behaviours and highlight the need for addressing mobility barriers to promote active and socially connected lifestyles, enhancing their well-being and quality of life. • Mobility motifs analysis of older people using 12-week smartphone tracking data. • 19 key patterns account for 92.2 % of daily travel networks among older people. • Novel matching with national ELSA data addresses self-selection bias. • Three mobility groups show distinct travel frequency and destination patterns. • Provides evidence-based recommendations for age-friendly transport planning.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".