Modélisation des interactions entre les modes de transport par l'intégration de sources diversifiées de données
Bibliographic record
Abstract
RÉSUMÉ: Cocktail transport, mobilité intégrée et multimodalité/intermodalité sont des termes de plus en plus répandus dans le domaine de la mobilité urbaine. En outre, la diversification des modes de transport est généralement présentée comme un moyen de réduire la dépendance à l’automobile, et donc de tendre vers une mobilité plus durable. Cependant, les bénéfices de cette plus grande disponibilité de modes restent à clarifier. Chaque mode étant habituellement analysé de manière indépendante, son rôle et ses interactions avec les autres moyens de transport sont encore mal compris. En particulier, des questions se posent sur le sens de la relation (complémentaire ou concurrentielle) entre les nouveaux services de mobilité partagée et le transport en commun. De même, peu d’outils ont été développés pour modéliser plusieurs modes simultanément. L’effet anticipé des stratégies de planification sur la répartition modale repose ainsi souvent sur des hypothèses simplificatrices. Ces lacunes sont notamment dues à des enjeux méthodologiques. En effet, plusieurs ensembles de données indépendants doivent être combinés pour pouvoir considérer tous les modes en même temps. De plus, des analyses longitudinales sont nécessaires afin de suivre la variabilité d’utilisation de chaque mode. Dans cette perspective, cette thèse vise à valoriser de multiples sources de données afin de qualifier et quantifier les interactions entre différents modes de transport urbains. Pour ce faire, la ville de Montréal est étudiée dans six articles. L’utilisation de plusieurs modes, classiques (automobile privée, transport en commun, vélo, taxi) et plus récents (autopartage, vélopartage), est décrite à partir de deux grands types de données : les enquêtes ménages déplacements de la région de Montréal, dites enquêtes Origine-Destination, et différents flux passifs (transactions, comptages, etc.). Les avantages (et inconvénients) de ces deux grands types de données ont souvent été confrontés dans la littérature mais peu de travaux ont tenté de les coupler. Pourtant, les informations multimodales contenues dans l’enquête et la longitudinalité des données passives sont deux caractéristiques intéressantes à rassembler pour l’analyse de l’utilisation de différents modes.» ABSTRACT: Mobility as a Service (or MaaS) and multimodality/intermodality are increasingly common terms in the field of urban mobility. Moreover, diversification of transport modes is generally presented as a way to reduce car dependency, and thus to move towards more sustainable mobility. However, the benefits of this greater availability of modes remain to be clarified. As each mode is usually analyzed independently, its role and interactions with other modes of transport are still poorly understood. In particular, there are questions about the direction of the relationship (complementary or competing) between new shared mobility services and transit. Similarly, few tools have been developed to model multiple modes simultaneously. The anticipated effect of planning strategies on modal split is thus often based on simplifying assumptions. These shortcomings are due in part to methodological issues. Indeed, several independent data sets must be merged to consider all modes at the same time. In addition, longitudinal analyses are needed to monitor the variability in the use of each mode. Within this context, this thesis aims to value multiple data sources in order to qualify and quantify the interactions between different urban transportation modes. To this end, the city of Montreal is studied in six papers. The use of several modes, both traditional (private car, transit, bicycle, taxi) and more recent ones (carsharing, bikesharing), is described using two main types of data: household travel surveys of the Montreal region, called Origin-Destination surveys, and different passive data streams (transactions, counts, etc.). The advantages (and disadvantages) of these two main types of data have often been compared in the literature, but few studies have attempted to link them. However, the multimodal information contained in the survey and the longitudinal nature of the passive data are two interesting characteristics to bring together for the analysis of the use of different modes.»
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".