Retail travel behavior across socio-economic groups: a cluster analysis of Brisbane household travel survey data
Bibliographic record
Abstract
Retail travel comprises about a quarter of all trips made in Australian cities, however these trips gain far less attention in transport planning than do journeys to work/ school. Accessibility is a major factor affecting travel behaviour, but socio-demographic characteristics are also important given research on factors influencing mode-choice. This paper explores retail travel behaviour in Brisbane, Australia, to identify differences in the influence of socioeconomic characteristics. The study uses the 2009 South East Queensland Travel Survey (SEQTS) 7-day household travel survey conducted in Brisbane to illustrate the quantity and characteristics of retail travel for different socioeconomic groups. The sample data has been divided into groups using cluster analysis techniques, which help inductively identifying meaningful subgroups (Hair et al., 1995). The data is analysed to show the major travel characteristics including: trip frequency; trip complexity; destination choice; and the mode share for each subgroup, allowing for comparative analysis. The results show that retail travel is the most unsustainable travel in terms of the proportion of car trips involved. Walking and public transport accounts for very few trips, but the number of these trips are subject to variations based on accessibility, type of trip, day of the week and socio-demographic characteristics. Shopping centres and supermarkets capture almost 50 percent of all shopping trips suggesting special attention on them is needed in terms of their function and location in the city. Low socioeconomic groups travel more frequently by walking and public transport to retail destinations. Young adults and families make significant numbers of trips to major shopping malls. This research underlines the role that retail form, urban form and socioeconomic characteristics play in determining retail travel behaviour. The results highlight notable differences in retail travel by subgroup. The implications are that interventions seeking to encourage sustainable retail travel behaviour, including spatial interventions and social marketing programs, should be carefully crafted to respond to these behaviours.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".