Cross-Border Travel Behavior Analysis of Hong Kong-Zhuhai-Macao Bridge Using MXL-BMA Model
Bibliographic record
Abstract
The Hong Kong-Zhuhai-Macao Bridge (HZMB) is an important transportation facility connecting Hong Kong, Zhuhai, and Macao. Thus, analyzing the characteristics of cross-border behavior becomes crucial for enhancing the smart travel experience of the HZMB. Discrete choice models (e.g., logit models) are commonly used to describe travel mode choice behavior. Multinomial logit (MNL) is subjected to the independence of irrelevant alternatives (IIA) assumption. Nested logit (NL) model does not consider the heterogeneity of travel individuals. Mixed logit (MXL) model can overcome the above limitations, but it may neglect model uncertainty. Therefore, a Bayesian model averaging (BMA) approach is applied to model travel mode choice behavior considering using revealed preference/stated preference (RP/SP) fusion data collected by questionnaires online. A structural equation model (SEM) is adopted to explore the potential relationship between latent variables, and two travel modes (i.e., cross-border bus and cross-border private car) are selected to analyze the cross-border travel mode choice of the HZMB. The results reveal that the MXL-BMA approach can better explain the cross-border travel mode choice behavior. And the transportation modes arriving and departing the HZMB have a significant impact on the travel mode choice of the HZMB. The findings of this study can provide suggestions for designing personalized travel services for travelers across the HZMB.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".