The Ethical Challenges and Professional Responses of Travel Demand Forecasters
Bibliographic record
Abstract
Thirty years ago scholars first presented convincing evidence that local officialsuse biased travel demand forecasts to justify decisions based on unstated considerations.Since then, a number of researchers have demonstrated convincingly that such forecastsare systematically optimisticâoften wildly soâfor reasons that cannot be explained solelyby the inherent difficulty of predicting the future. Why do modelersâprofessional engineers and planners who use quantitative techniques to predict future demand for traveland estimate its potential impact on built and proposed transportation facilitiesâgeneratebiased forecasts and otherwise tolerate the misuse of their work? On initial consideration, it is tempting to surmise that corrupt modelers are responsible for biased forecasting.Indeed, corruption is the most common explanation of forecasting bias and tales of mercenary behavior are all too common in the field. Data from in-depth interviews withtwenty-nine travel demand forecasters throughout the United States and Canada, how-2ever, suggest new and different ways to understand the suspect behavior of transportationplanning professionals.Those most likely to introduce bias and invite misuse of travel forecasts assumethat their technical analyses have little, if any, impact on policy making. For many, thisleads to disillusionment and requires responses to cope with feelings of marginalization.Others, untroubled by their apparent lack of influence, are complacent and need ways toavoid the ethical questions of practice. Both types of practitioners circumscribe professional roles and rely on the self-deceptive strategies of evasion and excuse making tomute their own disquieting realities that undermine positive concepts of self. The disillusioned wish not to see that they do not matter and the complacent that they do. Bias andmisuse seem to be the unintentional byproducts of these attitudes.Beyond enhancing the understanding of the systemic failures of travel demandmodeling, this research suggests practicable steps to reform and outlines an agenda forfuture work. Attention to these matters is important, not just to avoid expenditures onprojects and programs that cannot be justified on the basis of sound utilitarian calculations, but also to restore and preserve the credibility of a profession.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".