Mobility Tool Ownership and Peak Period Non-Work Travel Mode Choice
Bibliographic record
Abstract
The paper presents investigations of mode choice behaviour for peak period non-work trips. Purely non-work trips within the peak period represent a significant portion of peak period traffic. In the case of the Greater Toronto and Hamilton Area (GTHA), the study area of this investigation, around 11 percent of peak period trips are pure non-work trips and auto driving is the dominant mode. In fact, more peak period non-work trips are conducted using the auto passenger mode than all transit modes combined. This heavy auto dependency for pure non-work trips in the peak period, when transit service is at its highest throughout the day, requires an improved understanding of such mode choice behaviour. This paper uses data from a household travel survey collected in the GTHA to investigate peak period pure non-work trip mode choice in the context of household mobility tool ownership (auto and transit pass ownership). The paper also proposes an advanced econometric modelling approach for investigating mode choice behaviour for peak period pure non-work trips. The model involves systematic parameterization of the scale parameter of a generalized extreme value (GEV) model to capture both heterogeneity in choice behaviour and heteroskedasticity across the population. Empirical models highlight the capacity of the heteroskedastic model in capturing preference heterogeneity and the influence of household mobility tool ownership on peak period pure non-work trips. Results indicate that increasing transit pass ownership levels would be beneficial for increasing social welfare. In the case of transit services, better spatial coverage is more effective at attracting riders than increased service frequency.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".