Re-estimating UK Appraisal Values for Non-work Travel Time Savings Using Random Coefficient Logit Model
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».