This article considers RFID-based parking access logging together with short-term occupancy estimation from the collected data. In Uzbekistan, the growth of car ownership and the increasing pressure on parking spaces make manual access control less effective in practice. The paper treats RFID identification, LightGBM-based occupancy forecasting, Isolation Forest for anomaly detection, and a three-layer software-hardware architecture as one integrated solution. The edge layer uses Raspberry Pi, the application layer uses Django, and the data layer relies on PostgreSQL and Redis. The discussion also briefly covers the difference between UHF and HF RFID bands, protection with SHA-256 and a rolling nonce, the SFpark experience, and model selection based on the UCI Parking Birmingham dataset. The article aims to present parking as a system that can forecast load, not only record arrivals and departures.
| Mualliflar | Babadjanov , Elmurod, Makhambetjaliev , Musabek |
|---|---|
| Jurnal | Techscience.uz - техника фанлари долзарб масалалри |
| Nashr sanasi | 2026-05-14 |
| Jild | 4 |
| Son | 5 |
| Betlar | 24-29 |
| Til | O‘zbek |
| DOI | 10.47390/ts-v4i5y2026n04 |
DOI: 10.47390/ts-v4i5y2026n04 · Maqolaning asl sahifasi
RFID; parking; occupancy forecasting; machine learning; LightGBM; Isolation Forest; Django; Raspberry Pi; security; IoT; real-time monitoring, RFID; avtoturargoh; bandlikni prognozlash; mashina o'qitish; LightGBM; Isolation Forest; Django; Raspberry Pi; xavfsizlik; IoT; real vaqt nazorati.
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