An IoT Based Traffic Management System Using Drone and AI
Bibliographic record
Abstract
Management of ground traffic on both urban streets and highways in a smart city setting requires collecting a huge amount of logistical data. Accessing real-time information of the traffic is essential in the event of an emergency. It requires the traffic control center to regularly monitor flows of vehicles and take suitable actions to reduce traffic jams. Several tiny devices are needed to collect and transmit real-time data from different locations. However, the bandwidth and power consumption of each device is very limited. Therefore, it is essential to utilize an efficient algorithm which reduces the bandwidth, as well as power consumption. In this paper, an efficient method is proposed to reduce the transmission bandwidth while keeping the quality of the videos acceptable for image processing on the server end. To evaluate the performance of the algorithm, a framework to monitor and control the traffic on highways is developed. This framework uses a drone to fly over the traffic to capture the logistical information, and then send real-time video to the server. An object detection algorithm empowered by artificial intelligence (AI) is implemented on the cloud server that detects the number of type of vehicles, and accordingly makes decisions to manage traffic flow.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".