A Wireless Mesh Network Platform for Vehicle Positioning and Location Tracking
Bibliographic record
Abstract
Large urban areas in North America as well as many other parts of the world are experiencing unprecedented and soaring congestion problems. It is imperative that modern societies upgrade their transportation systems in order to remain competitive, and maintain the high quality of life and social wellbeing. Current practices in Intelligent Transportation Systems (ITS) data gathering are dominated by the use of point detectors for surveillance, and wire-line communication networks for data transmission. Reliance on point detectors is losing appeal due to detector reliability issues, the cost of building and maintaining detector networks, and potential traffic disruption during construction and maintenance of these networks. This chapter describes a novel wireless mesh network platform for traffic monitoring. The platform uses traveling cars as data collection probes and uses wireless municipal mesh networks to transport sensed data. The platform assumes that cars or drivers’ mobile devices are equipped with the widely adopted low-cost Bluetooth wireless technology. Field trials of the proposed platform demonstrated its capability to track cars traveling at speeds of 0 to 70 km/hour. The platform was able to track cars as they travel and turn on a typical road network. In addition, the platform was used to approximate car speeds through determining the change in position in a time period. The preliminary results indicated an accuracy of ± 10%- 15%. The chapter describes the architecture, implementation, and field-testing of the proposed platform. It also discusses aspects of large-scale deployment of the proposed platform to cover large geographic areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".