Scholarlink

<p>The issue of parking management in university campuses has continued to face challenges owing to space constraints and the absence of real-time information. This problem is addressed by the proposed solution in this study, which is robust and intelligent and specifically designed for university campuses. This paper advances real-time campus parking through three innovations based on established You Only Look Once, Version 8 (YOLOv8) and Raspberry Pi tools: (1) campus-specific YOLOv8n fine-tuning (94.2% mean Average Precision (mAP), 450ms on Pi 4B); (2) adaptive Message Queuing Telemetry Transport (MQTT). Quality of Service (QoS) reducing 20% packet loss; and (3) S3-integrated forecasting producing 45% simulated efficiency gains. A user-friendly, web-based dashboard offers live parking updates to students, faculty, and visitors. This allows users to check space availability before arriving and reduces search time. The modular architecture supports decentralized deployment, with each Raspberry Pi independently managing a designated parking zone. The design is inherently scalable, enabling additional sensors and cameras to be added as needed to cover larger or more complex parking areas. To ensure privacy and reduce bandwidth use, live video and image access are restricted to management, maintaining data security and network efficiency. By combining edge computing, sensor fusion, and cloud services, the proposed solution enhances automation, improves user experience, and advances smart campus initiatives. This framework provides a scalable, adaptable model for modernizing parking infrastructure in educational institutions and beyond.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Smart mobility infrastructure: improving campus parking efficiency in real time

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月15日

查看全文
  • JS Jefflin Deno ,
  • S Karthi Sree,
  • S Maheswari,
  • P Sasikumar

摘要

The issue of parking management in university campuses has continued to face challenges owing to space constraints and the absence of real-time information. This problem is addressed by the proposed solution in this study, which is robust and intelligent and specifically designed for university campuses. This paper advances real-time campus parking through three innovations based on established You Only Look Once, Version 8 (YOLOv8) and Raspberry Pi tools: (1) campus-specific YOLOv8n fine-tuning (94.2% mean Average Precision (mAP), 450ms on Pi 4B); (2) adaptive Message Queuing Telemetry Transport (MQTT). Quality of Service (QoS) reducing 20% packet loss; and (3) S3-integrated forecasting producing 45% simulated efficiency gains. A user-friendly, web-based dashboard offers live parking updates to students, faculty, and visitors. This allows users to check space availability before arriving and reduces search time. The modular architecture supports decentralized deployment, with each Raspberry Pi independently managing a designated parking zone. The design is inherently scalable, enabling additional sensors and cameras to be added as needed to cover larger or more complex parking areas. To ensure privacy and reduce bandwidth use, live video and image access are restricted to management, maintaining data security and network efficiency. By combining edge computing, sensor fusion, and cloud services, the proposed solution enhances automation, improves user experience, and advances smart campus initiatives. This framework provides a scalable, adaptable model for modernizing parking infrastructure in educational institutions and beyond.