Tools and Methods for a Transportation Household Survey
Notice bibliographique
Résumé
INTRODUCTION Large household always have presented a methodological challenge for transportation planners and authorities. Conducting a survey of more than 70,000 households is not a simple task because of the sample size and the complexity of the survey itself. Every planner knows that transportation data are strongly related to the spatial elements of a territory and to the transportation network (roads and public transit), and that the survey tool must take these specificities into account. Today, even though intelligent transportation systems (ITSs) have provided new ways to collect data, large transportation still are needed. Data collected from these operations now are well integrated in the fields of transportation planning, finance, and management. This paper presents the information technologies that were used for the 2003 Greater Montreal Area Household (Quebec, Canada). It also emphasizes the technological background and architectures that were required to yield the best results possible from the survey. Following a recounting of the history of the household survey in the Montreal area, the totally disaggregate approach and transportation object-oriented modeling, two key elements that helped support and develop the 2003 tools, are presented in the background section. The third part of the paper, Survey Information System Framework, describes the methodology that was used to prepare and synchronize the various software programs and databases. The Implementation section is aimed at demonstrating the functions of the software that was used for the survey. The conclusion reports some findings on the 2003 experience in Montreal. BACKGROUND In the past, travel were conducted mainly by mail or face-to-face interviews. They basically provided data for the development of aggregated travel forecasting models. Richardson et al. (1995) propose a thorough description of classical Methods for Transport planning. With the advent of new technologies, combining spatial information systems and computation capacities, travel have become an integral part of the continuing transportation planning process and assist many types of transportation studies. In the Transportation Research Board Millennium Paper of the Committee on Travel Surveys Methods, Griffiths et al. (2000) identify future directions for travel survey methods: * The improvement of the quality standards of travel through full and honest documentation of the survey process. The need to document all stages of the survey process also appears as the most overriding conclusion of a conference held in 1997 on raising the standards of travel (Richardson 2002). * The use of mixed-mode survey designs to meet the data needs of the surveyor in ways that create the least burden and the greatest flexibility for the respondents. The concept of common cognitive space between an interviewer and a respondent was outlined by Brog (2000). The purpose of survey tools is to maximize this common space to facilitate the exchange of information between the two agents and to lessen the respondent burden. * A move toward a more continuous survey to provide more timely data in an economical manner, which also would develop and preserve technical and managerial skills in the conduct of complex surveys. * The judicious use of new technologies to augment existing survey techniques. In this regard, computer-assisted telephone interviewing (CATI) is one of the main fields of development regarding travel surveys. It allows interviewers to administer a survey questionnaire via telephone and capture responses electronically. CATI employs interactive computing systems to assist interviewers and their supervisors in performing the basic data-collection tasks of telephone interview surveys (Nicholls II 1988). …
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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
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