A Three-Pillar Methodology and Framework for Seamlessly Integrated Cyber-Physical Intelligent Transportation System of Systems
Notice bibliographique
Résumé
Novel smart city initiatives rely on information and communications technologies to manage mobility in metropolitan areas. The proliferation of mobile devices, wearables, and connected vehicles has resulted in many dynamic mobility applications that offer isolated and segregated services covering different needs of the transportation system such as advanced traveller information, smart parking management, and integrated dynamic transit services. The majority of these applications focus on solving a single problem; hence they offer limited support to provide regional, multi-modal, and multi-jurisdictional services capable of meeting the rising expectations of travellers. A contemporary challenge in intelligent transportation applications is to amalgamate sensors, services, and city infrastructure into an integrated intelligent transportation system of systems where isolated applications are seamlessly combined to render integrated mobility services to stakeholders and end users. The integrated system of systems provides a unifying framework to support the automatic formulation of regional and integrated ITS services in response to different situational changes. This thesis proposes a coordination and integration framework based on semantic web technology that supports day-to-day intelligent transportation operations in smart cities in the context of Internet of Things (IoT). The framework defines three pillars to coordinate and integrate dispersed cyber and physical components, provide higher order mashed ITS services, and facilitate collaboration, coordination, and knowledge sharing across different city stakeholders. The first pillar of the framework is the Ontological Semantic Knowledge Representation (OSKR) pillar which reduces the conceptual and terminological confusion involved in the coordination process by introducing a four-tier ontological model of: (1) abstract ITS processes based on the Canadian ITS Architecture, (2) web services, (3) ITS sensors and (4) transportation infrastructure. The overall ontology describes the shared concepts and their relationships in a machine-understandable, uniform and consistent manner. The second pillar of the framework is the Integrated Service Planning (ISP) pillar which orchestrates, based on the collaboration of stakeholders, the composition of the cyber-physical resources satisfying the objectives, constraints and conditions required by the envisioned higher order intelligent transportation operation. The ISP pillar identifies the characteristics of the integrated application including needs and scope, key functionalities to be integrated, functional requirements, interfaces and key hierarchical tasks. The third pillar of the framework is the real-time Integrated Service Execution (ISE) pillar which enables the creation and execution of hierarchical task networks, hierarchical service discovery and invocation/execution to ultimately provide the composite integrated higher order ITS service. The ISE pillar also provides the level of abstraction required to manage the heterogeneity of the shared cyber-physical components offering several functionalities such as data mapping, message routing, and message validation. This thesis presents the three pillars of coordination and demonstrates how they can be used to enable the dynamic provisioning of advanced traveller information services within the Greater Toronto Area.
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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 | 0,010 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,013 |
| Communication savante | 0,011 | 0,010 |
| Science ouverte | 0,005 | 0,007 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».