Re-estimating UK Appraisal Values for Non-work Travel Time Savings Using Random Coefficient Logit Model
Bibliographic record
Abstract
The official appraisal values of travel time savings (VTTS) for non-work trips in United Kingdom (UK) were estimated by very basic discrete choice model back in 2001, based on the stated choice (SC) data collected nearly over 20 years ago. This choice model developed by Bates and Whalen (2001) was specified to address the long-standing issues in the field of VTTS valuation including the sign (i.e., gains vs. losses) and the size (i.e., small time savings) of the VTTS, as well as allowing continuous interactions between VTTS and other journey covariates (i.e., income and journey distance/cost). With the respect to the size of the VTTS, the 2001 Study found that a “tapering” function, whereby time changes are increasingly discounted, could best explain the lower unit utility observed for small time savings (STS). This method effectively adjusts the indifference curve for travel time changes within a fixed threshold, albeit a caveat that this “perception effect” (as if the respondent perceived a smaller time change in the SC experiment) is contrary to the theoretical expectation of the shape of the indifference curve. While this base VTTS in UK remains unchanged in real terms, the field of discrete choice modelling had evolved significantly in the past decade brought primarily by a leap of computing power and improved simulation techniques. Random coefficient models such as Mixed Multinomial Logit (MMNL) has been widely used to facilitate more realistic modelling of travel behavior by explaining random taste heterogeneity across respondents that cannot be done in a deterministic manner. Furthermore, techniques in specifying these advanced models for VTTS valuation such as the treatment of counter-intuitively signed coefficient in the utility formulation are also well known to researchers nowadays. This paper is then to apply the MMNL model to explain random taste heterogeneity and re-estimate the current UK VTTS within a random coefficient framework based on the utility formulation set out in the 2001 Study. Along with the theoretical discussions, this paper presents a synthesis of empirical evidence to support an updated appraisal value for non-work travel time savings in UK. The key findings from this paper include a much higher mean value for the VTTS estimate, as well as a significantly reduced “perception effect” for the STS. In particular, this research found that the MMNL model substantially reduces the “tapering” parameter of the discounting function for STS such that the “perception effect” of the VTTS for STS becomes minimal. This finding appears to support the argument that travel benefits due to STS should be included for transport appraisal and challenges the current appraisal framework for countries including Germany and Canada in which the VTTS is discounted or even completely ignored for STS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".