Examining the Anticipated Integration of Bikeshare with Travel Modes: Latent Class Model Application
Bibliographic record
Abstract
This paper presents a comprehensive investigation of anticipated bikeshare integration with available travel modes in an urban setting. The key objective of the research is to enhance our understanding of the factors affecting choice of different potential integration types that include (a) transit-bikeshare, (b) auto-bikeshare, (c) walk-bikeshare, (d) other modes-bikeshare, and (e) no integration with bikeshare, if a public bikeshare system is available. This paper uses data collected from a bikeshare survey conducted in Halifax, Canada, in 2011. The web-based survey included questions regarding the system’s potential usage in respondents’ daily activities, the frequency of usage, and the anticipated integration with their existing travel mode choices. A latent class choice model is used in the study, which accounts for unobserved heterogeneity often ignored in traditional choice modeling. One of the unique features of the modeling approach taken in this paper is that observed attitudinal factors determine class membership probabilities. The results suggest that the latent class logit model outperforms the conventional logit model in terms of model fit. Several socio-economic characteristics, accessibility measures, and neighborhood characteristics are found to explain different types of integration. Moreover, it is found that considerable latent heterogeneity exists among sampled household. The research offers significant behavioral insights that could be potentially useful in planning for bikeshare implementation.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| 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".