Current issues of public transportation
Notice bibliographique
Résumé
Current issues of public transportationFor several decades, public transportation has played an important role in the context of sustainability and efficiency of metropolitan's transportation system.However, the concerns and issues of the public transportation system have been changed as society and/or technologies do.This special issue on ''Current Issues of Public Transportation'' is a collection of selected papers presented at the International Seminar on Public Transportation held on February 20, 2006 at the University of Seoul, Korea and some other papers.The seminar placed greater emphasis on discussions the emerging issues of public transportation around the world.The seminar brought together experts in demand modeling of transit systems worldwide as well as scholars in public transportation-related accident analysis and bus information system.The selected papers cover wide spectrum of emerging issues of public transportation from three Asian countries and Canada.The Seoul Metropolitan Area (SMA) has changed the paradigm of transportation policy from caroriented one to public-oriented one.Inspired by the well-known Downs-Thomson's paradox, Lee and his colleagues [DOI: 10.1002/atr.101]analyzed the travel patterns based on social changes between 1996 and 2002, and then identified main implications in transport policies in the SMA, Korea.They examined the general travel pattern changes in the SMA and compared the travel patterns of regions invested in road construction (road-invested areas) with those of regions invested in transit (transitinvested areas).They showed that while road investment had little effect on reducing congestion, the number of cars decreased in transit-invested areas due to the modal shift to transit modes.It was suggested that transit-oriented policies should be utilized as a solution to overcome severe traffic congestion.Taxi service can be seen as a higher level of public transit, by providing personal, on demand, pointto-point transportation to persons whose value of time, and comfort requirements are considerably higher than those of bus and trains users.Kattan et al. [DOI: 10.1002/atr.102]developed two regression models for work trips made by taxi for the year 1996 and the year 2001, respectively for 25 Canadian cities.The developed regression models indicated the primary factors that influenced work commuting by taxi.Two major factors were identified: the total number of work trips made by public transit and the total number of low-income households.The 2001 regression model indicated an increase of the value of the transit commuter's coefficient from its 1996 figure.They highlighted the important role that taxis play in (i) decreasing the demand for parking especially in urban cores and (ii) serving the transportation disadvantaged population especially in outlying areas poorly served by public transport.In a related study from Thailand, Park et al. [DOI: 10.1002/atr.103]analyzed the impacts of the enhancement of access modes to the main water public transportation in Bangkok along the Chao Phraya River.In order to achieve this purpose, access mode choice behaviors were modeled using the Probability Distribution Function (PDF) model, the Multinomial Logit (MNL) model, and the Nested Logit (NL) model.They analyzed the catchment areas for different access modes and the factors affecting them.Factors affecting the extent of the catchment area such as main haul distance were evaluated.The NL model was found to be the most suitable one for modeling access mode choice behavior.It was also found that reducing in-vehicle travel time, waiting time and/or cost of the bus ride gave most significant impact on the enhancement of access modes.Barua and Tay [DOI: 10.1002/atr.104]paid their attention on the transit-related safety issues in Bangladesh.Using the ordered probit model on bus crash data from 1998 to 2005 in Dhaka, Bangladesh, they showed that there is a general increase in the severity of transit bus crashes over this period.In addition it was found that crash severity tends to increase when the collision occurs on
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 | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,004 | 0,009 |
| Communication savante | 0,013 | 0,023 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,009 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,037 | 0,007 |
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 ».