Outils de visualisation de données de cartes à puce pour une société de transport collectif
Notice bibliographique
Résumé
Public transit authorities are choosing more and more smart card automated fare collection systems and realize that those daily recovered data, since 2008 for the greater Montreal region, have a great potential for their planning and operations.In this context, this research master is part of a global project held for three-year period in collaboration with various partners.It follows previous research works on data enrichment of smart card transactions by combining their trip origin and destination.For the purpose of this project, the transit authority RTL (Rseau de Transport de Longueuil) provided one month (March 2013) of bus and metro smart card transactions (3.1 million).As far as Thales is concerned, they made available their "Analytics For Transportation" portal developed by its CeNTAI Department (Centre de Traitement et d'Analyse de l'Information).The main objective of this master research is to design interfaces for viewing and analyzing smart card transactions, enriched of their destination, while meeting the needs of a transit operator.The sub-objectives, corresponding to the steps of this research, are:-Make operational the algorithm determining trip destinations -Conceptualize the most adequate data structure enabling their visualization -Design visualization interfaces meeting the needs of a transit operator This thesis starts with a literature review with, on the one hand, the previous works on the estimation of the trips origin and destination, and, on the other hand, other projects on data visualization.The steps followed to meet the above three sub-objectives are described in the methodology section.The final section presents the results and analysis obtained from these enriched data.The main achievements of this project are:-The optimization and redesign of the algorithm estimating trip destinations and its adaptation to a network defined with the GTFS format (General Transit Feed Specification)The presentation of ergonomic insights, obtained thanks to the use open source tools (Elasticsearch, Kibana), enabling those enriched smart card data to be quickly analyzed viii -The design of a new customized web interface developed to present other key indicators used by a public transport companyIn conclusion, this research project presents an operational solution, which for a set of smart card transaction data offers, in one step, to estimate the destination of each smart card transaction trip, to prepare additional statistics (distance and travel time, trip-leg sequences ) and to export those enriched transactions to a text file or a data base (Elasticsearch).The whole process is made within a relatively short time: 20 minutes for 3 million transactions, export time included.The data is then directly available and usable in web portals configured or developed for the occasion and which take into account the needs of the customers.Of the 3.1 million available transactions, 20% are metro transactions.These transactions help the algorithm in the estimation of a trip destination.These metro transactions only help to find 1 more percent of destinations, resulting in 79% of trip destinations recovered for our March 2013 dataset.Trip-legs have also been reconstructed by the algorithm.It shows for example that 66% of bus travels are made without a transfer.The share of users making only one transfer represents respectively 12% from bus to bus and represents 20% from bus to metro.In the end, this research shows that the analysis of large volume of data within a limited period of time is possible and an operational solution is presented.Indeed, it would require a processing time of 32 hours to enhance the RTL smart card transactions of the last 8 years, with 3 million transactions per month.These OD type of data would then be available to power the analysis of the various departments of a public transit authority such as operations, planning and even marketing and finance.The developed visualization prototypes would then help the RTL in drafting the specifications of a new tool sold and designed by a company selling BI (Business Intelligence) solutions to visualize their business data.
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 distillée sur la base complète
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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».