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<p>Advancements in real-time tracking technologies have rapidly transformed the transportation sector. This paper presents Vahan Samvedak, an advanced real-time vehicle tracking and safety system developed specifically for college campus transportation. This advanced vehicle tracking system is designed to ensure that things become safer and drive in a smoother manner. The majority of the systems do not employ complex GPS or RFID. Instead, this solution is a combination of IoT and multi-modal AI with an emphasis on GPS tracking, emergency notifications, passenger counting, and AI features used in security (facial recognition and drowsiness detection). The system is developed using OpenCV, TensorFlow, and PyTorch. Tests of performance have shown that the GPS is accurate to within 2.5&#xa0;m and that alerts can be sent in less than two seconds. The authentication and drowsiness detection systems were 95% and 97.81% accurate, respectively. The system had 99.2% uptime and cut wait times of students by 68% in a test with five campus buses. These results show that Vahan Samvedak is a good choice for smart campuses and other public transportation uses because it gives users a more reliable and safe experience than regular trackers.</p>

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Vahan Samvedak: a comprehensive real-time college bus tracking and safety system

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月16日

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  • Yash Chauhan,
  • Sujal Dhiman,
  • Shubham Negi,
  • Jigyasa Bamola,
  • Kanika Pandey,
  • Neha Tripathi,
  • Shweta Goyal,
  • Sandeep Gupta,
  • Mukesh Kumar

摘要

Advancements in real-time tracking technologies have rapidly transformed the transportation sector. This paper presents Vahan Samvedak, an advanced real-time vehicle tracking and safety system developed specifically for college campus transportation. This advanced vehicle tracking system is designed to ensure that things become safer and drive in a smoother manner. The majority of the systems do not employ complex GPS or RFID. Instead, this solution is a combination of IoT and multi-modal AI with an emphasis on GPS tracking, emergency notifications, passenger counting, and AI features used in security (facial recognition and drowsiness detection). The system is developed using OpenCV, TensorFlow, and PyTorch. Tests of performance have shown that the GPS is accurate to within 2.5 m and that alerts can be sent in less than two seconds. The authentication and drowsiness detection systems were 95% and 97.81% accurate, respectively. The system had 99.2% uptime and cut wait times of students by 68% in a test with five campus buses. These results show that Vahan Samvedak is a good choice for smart campuses and other public transportation uses because it gives users a more reliable and safe experience than regular trackers.