Wi-Fi Service-Oriented Framework for ITS Infrastructure Communication and Monitoring
Bibliographic record
Abstract
New trends in software systems engineering and wireless communications open up new horizons for innovative ITS infrastructure and info-structure. In this paper, we introduce and describe a new service-oriented platform for ITS monitoring, communications and wireless applications development. The framework consists of software and networking platforms. The state of the art service oriented software platform can be used by ITS application developers as a modular environment to develop their ITS software applications. Latest trends in software development methodologies such as enterprise service oriented development and design were followed to establish this framework. In addition, the new framework encapsulates a novel and cost effective ITS networking platform that uses traveling cars as probes for monitoring traffic and ITS infrastructure. The networking platform is built using common hardware (bluetooth and Wi-Fi) and open source software. In addition, the networking platform exploits the already-deployed (municipal) wireless mesh networks (e.g., WI-FI-based WMN) to collect and transport ITS data, across the WMN and ultimately through the internet, to a monitoring center. We describe the network and software architecture including the wireless protocols and their interactions that we used to build our platform. The developed platform can be extended to provide many applications and services such as congestion identification and quantification, traveler information systems, and navigation and route guidance services. This paper illustrates how our platform is used to detect and track vehicles and measure their approximate speeds as a proxy for congestion level. The data collected at the hardware level is wrapped and presented as services with standard access interfaces. Our pilot field tests and results in Regina, Canada are encouraging. We plan to incorporate more advanced algorithms to enhance speed calculation accuracy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".