A Three-Pillar Methodology and Framework for Seamlessly Integrated Cyber-Physical Intelligent Transportation System of Systems
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".