Generation and quality assessment of route choice sets in public transport networks by means of RP data analysis
Notice bibliographique
Résumé
This paper will describe how modeling route choice presumes the generation of a choice set of alternatives that are perceived as available and then are chosen from by travelers. Recent research has posed increasing attention toward the importance of size and composition of choice sets in route choice modeling, and has shown that the generation technique has great impact on route choice model estimates and predictions. This paper has limited knowledge concerning the actual route choices of passengers in public transport networks and the evaluation of the quality of generated choice sets with respect to real life choices. One of the reasons lies in the difficulty to collect data on actual route choices in public transport networks, since a lot of information has to be provided to describe the routes actually used by travelers. In fact, while for private transport it is possible to use global positioning system (GPS) devices to track routes and then map the data to a physical network, for public transport the same method is of little help because relevant information about the lines used is not retrievable with these devices. A problem with GPS is also that signal fall outs in tunnels (metro and sections of the urban rail system). Moreover, GPS devices do not allow obtaining information on the trip purpose, which is another fundamental piece of information for uncovering route choice determinants. This study relies on approximately 2,000 observations of actual route choices in a public transport network, which have been collected by means of a detailed questionnaire gathering all relevant trip information about routes, lines and purposes. The method for choice set generation in a public transport network is based on a timetable probit-based stochastic transit assignment model based on MSA. The method defines a doubly stochastic function that accounts for heterogeneity in both perceived costs and individual preferences. Moreover, the method considers similarities across alternatives and differences in feeder modes. The complexity of the route choice of public transport passengers is therefore represented with a high level of detail. The generated choice sets are tested in terms of quality and number of attractive routes by comparing them with the observed choices. Considering each origin and destination (OD)-pair, attractive routes are defined theoretically as alternatives that travelers would consider, and operatively as the set of alternatives actually selected by all travelers sharing that specific OD-pair. Furthermore, the choice probabilities in the generated choice sets are compared to the actual choice probabilities from the route choice observations. For assessment of the quality of the choice sets, data from the Danish Travel Behavior Survey are used. This survey collects detailed information about route choices of public transport passengers in the Greater Copenhagen area. The dense public network includes trains (regional, urban, local rail), metro and buses (high class and regular). Most travelers have many possible alternative routes, as a result of the combinatorial nature of the problem of combining different modes and different lines. Because of this combinatorial problem, in this dense public network the number of alternative routes can be very high, even though not all possible routes are relevant and attractive to travelers.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,021 | 0,074 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».