Modeling Commuting Mode Choice with Explicit Consideration of Carpools in the Choice Formation: A Case Study in Alberta
Bibliographic record
Abstract
This paper investigates the option of considering carpool as an effective Travel Demand Management (TDM) tool. Carpool is already an existing urban commuting mode with a modest modal share (around 9 percent in many North American cities). However, with increasing interests in considering Carpool as an effective TDM tool for long-term sustained TDM program, there is a growing interest in critical investigation of the carpool mode choice. This paper investigates the carpool mode choice in the context of overall commuting mode choice preferences. The paper uses a hybrid econometric modelling technique of jointly modelling carpool consideration as well as commuting mode choice with response bias correction through the accommodation of measurement equations. Cross nested error structure is used to capture correlations among various existing commuting modes and carpool consideration in the choice set. Empirical models are estimated by using a large data set collected through a commuter survey by the City of Edmonton among its own employees. This specific employer-based data set is used to investigate complementary and supplementary effects of various TDM tools compared to carpool as a TDM tool. The results reveal that interactions between various TDM tools with carpool can be different at different level of decision making (choice set formation level and final choice level). The paper contributes carpool mode choice. It also contributes to TDM evaluation literature by unraveling critical behavioural elements to develop effective TDM programs.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".