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Smart city article details
| Title | Internet Of Things Based Parking Slot Detection And Occupancy Classification For Smart City Traffic Management |
|---|---|
| ID_Doc | 32970 |
| Authors | Alkhudhayr H. |
| Year | 2025 |
| Published | Engineering Applications of Artificial Intelligence, 152 |
| DOI | http://dx.doi.org/10.1016/j.engappai.2025.110802 |
| Abstract | With the rising issue of traffic congestion, there is a growing need for smart parking management systems. The goal is to support the idea of smart city traffic management by providing real-time data on the occupancy of outdoor and interior parking slots. It is a huge problem, therefore, to find a cost-effective image-based detection technology that can replace costly sensor-based technologies. This research presents a vision-based parking slot identification and occupancy categorization system that is both very accurate and built to work in real-time, and it is powered by the Internet of Things (IoT). Using the Improved Weight-Based White Shark Optimizer (IW-WSO) to further tune the hybrid Single Shot MultiBox Detector (SSD)-ShuffleNet model, the system is constructed to boost computing efficiency while preserving detection robustness across different environmental circumstances. The SSD-ShuffleNet model generates bounding boxes and confidence scores for parking spaces, while ShuffleNet ensures lightweight and computationally efficient processing. With an optimum support vector machine (OSVM) fine-tuned with IW-WSO, in this current research can detect the occupancy status (occupied or unoccupied) with a high accuracy of 96.5 %, which is better than typical deep learning-based approaches. Reliable performance in varying lighting and weather circumstances is shown by further performance parameters, which include a recall of 95.1 %, an AUC of 0.97, and an accuracy of 94.8 %. Operational in real-time on IoT devices, the system runs at an average inference time of 50 ms per frame. Congestion reduction, improved urban mobility, and sustainable smart city growth are all goals of the suggested framework, which provides a scalable and cost-effective solution for smart parking systems. Integrating this technology into current intelligent transportation systems is made possible by the findings, which show that it is resilient across a variety of environmental circumstances. © 2025 Elsevier Ltd |
| Author Keywords | Artificial intelligence (AI); Hybrid single shot multibox detector-shufflenet model; Improved weight-based white shark optimizer; Parking slot detection; Single shot MultiBox detector; Smart city; Support vector machine; Traffic management |
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