Development of an Employer-Based Transportation Demand Management Strategy Evaluation Tool with an Advanced Discrete Choice Model in Its Core
Bibliographic record
Abstract
This paper presents a tool for the evaluation of employer-based transportation demand management (TDM) strategies. The conventional method of evaluating TDM strategies has typically been to conduct expensive before-and-after strategy implementation surveys. As an alternative approach, this research uses a joint revealed preference (RP) and stated preference (SP) survey (the RP–SP survey) administered before deployment of the TDM strategy, which is more cost-effective and efficient. The data collected from the RP–SP survey were used to estimate an advanced discrete choice model, which was packaged into a spreadsheet-based tool for TDM decision support. The tool adopted the concept of penetration rate, whereby only a subset of the target population could be targeted for any specific TDM strategy. The tool that was developed provides an alternative approach for the predeployment evaluation of any TDM strategy for efficient implementation. Moreover, the empirical model used in the tool reveals many behavioral details about commuters’ responses to employer-based TDM strategies.
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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 | 0.006 | 0.000 |
| 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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".