Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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Implementation of a real-time public transportation monitoring system

실시간 대중교통 모니터링 시스템 구현

  • Eun-seo Oh (Computer and Artificial Intelligence Engineering, Pukyong National University) ;
  • So-ryeong Gwon (Computer and Artificial Intelligence Engineering, Pukyong National University) ;
  • Joung-min Oh (Computer and Artificial Intelligence Engineering, Pukyong National University) ;
  • Bo Peng (School of Artificial Intelligence Convergence, Pukyong National University) ;
  • Tae-kook Kim (School of Computer and Artificial Intelligence Engineering, Pukyong National University)
  • 오은서 (국립부경대학교 컴퓨터.인공지능공학부) ;
  • 권소령 (국립부경대학교 컴퓨터.인공지능공학부) ;
  • 오정민 (국립부경대학교 컴퓨터.인공지능공학부) ;
  • 펑보 (국립부경대학교 인공지능융합학과) ;
  • 김태국 (국립부경대학교 컴퓨터.인공지능공학부)
  • Received : 2024.07.03
  • Accepted : 2024.08.13
  • Published : 2024.08.31
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Abstract

In this paper, a real-time public transportation monitoring system is proposed. The proposed system was implemented by developing a public transportation app and utilizing optical sensors, pressure sensors, and an object detection algorithm. Additionally, a bus model was created to verify the system's functionality. The proposed real-time public transportation monitoring system has three key features. First, the app can monitor congestion levels within public transportation by detecting seat occupancy and the total number of passengers based on changes in optical and pressure sensor readings. Second, to prevent errors in the optical sensor that can occur when multiple passengers board or disembark simultaneously, we explored the possibility of using the YOLO object detection algorithm to verify the number of passengers through CCTV footage. Third, convenience is enhanced by displaying occupied seats in different colors on a separate screen. The system also allows users to check their current location, available public transportation options, and remaining time until arrival. Therefore, the proposed system is expected to offer greater convenience to public transportation users.

본 논문에서는 실시간 대중교통 모니터링 시스템을 제안하였다. 제안한 연구는 대중교통 앱(App)을 제작하고, 광센서, 압력 센서, 객체검출 알고리즘을 활용하여 구현하였다. 또한, 버스 모형을 제작하여 동작을 검증하였다. 제안한 실시간 대중교통 모니터링 시스템은 다음과 같이 3가지 특징을 가진다. 첫째, 광센서와 압력 센서의 값의 변화에 따라 좌석의 착석 여부와 총 승객 인원을 파악하여 앱에서 대중교통 내부의 혼잡도를 확인할 수 있도록 구현하였다. 둘째, 다수의 승객이 동시에 승하차할 때 발생할 수 있는 광센서의 오차를 방지하기 위해, 객체검출 알고리즘인 YOLO를 활용하여 CCTV 승객 수 확인 가능성을 확인하였다. 셋째, 별도의 화면에서 탑승할 버스 내의 좌석이 착석된 경우를 색깔로 표시함으로써 편의를 제공한다. 승객의 현재 위치 확인, 현 위치에서 탑승 가능한 대중교통 및 도착 잔여 시간도 확인 가능하다. 따라서 제안한 시스템은 대중교통 이용객들에게 보다 높은 편의성을 제공할 수 있을 것으로 기대한다.

Keywords

Acknowledgement

이 논문은 2023학년도 국립부경대학교 산학협력단의 지원을 받아 수행된 연구임(202311680001).

References

  1. S.R.Gwon, E.S.Oh, J.M.Oh, T.K.Kim, "A Study on Real-Time Public Transportation Information," Korea Multimedia Society Spring Conference, Vol.26, No.1, pp.107-108, 2023.
  2. Arduino[Internet], https://www.arduino.cc.
  3. MIT App Inventor[Internet], https://appinventor.mit.edu.
  4. B.J.Lee, J.K.Kim, K.S.Kim, S.H.Oh, "Stated Preference Analysis of the Impacts of Bus Crowdedness Information on Bus Choice," Journal of Korean Society of Transportation, Vol.26, No.6, pp.61-70, 2008.
  5. Y.R.Jeong, S.H.Bae, "Estimation of Bus Passenger Occupancy and Degree of Congestion by Using Bus Card Data in Busan," Journal of Transport Research, Vol.22, No.3, pp.13-24, 2015. https://doi.org/10.34143/JTR.2015.22.3.13
  6. Y.J.Jin, Y.M.Kim, D.H.Lee, S.H.Bae, "CCTV video data to estimate the number of people in the bus and determine the congestion level," Journal of Korean Society of Transportation, No.78, pp.297-302, 2018.
  7. J.H.Lim, S.S.Park, S.B.Go, J.T.Kim, D.H.Kim, "Bus Seat Availability Prediction System based on Time Series Forecasting," Journal of KIISE(Korean Institute of Information Scientists and Engineers), pp.1873-1875, 2017.
  8. W.S.Choi, J.H.Jo, W.C.Park, S.H.Choi, "Design of YOLO-NAS Model for Safety Helmet Detection in Embedded Systems," Journal of the Korea Contents Association, Vol.24, No.3 pp.14-24, 2024.
  9. M.H.Park, J.H.Choi, W.J.Lee, "Object detection for various types of vessels using the YOLO algorithm", Journal of the Korean Society of Marine Engineering, Vol.48, No.2 pp.81-88, 2024.
  10. H.D.Lee, S.G.Kim, S.C.Na, J.Y.Ham, C.H.Kwak, "A Study on the Detection of Traffic Disadvantaged Persons Using Ensemble YOLOv5 Model," Journal of the Korea Computer Information Society, Vol.29, No.1, pp.61-68, 2024.
  11. D.W.Kim, J.H.Lee, "Automated Banner Crackdown System Using YOLO and OpenCV Technology," Journal of the Society of Semiconductor and Display Technology, Vol.22, No.4, pp.48-52, 2023.
  12. S.W.Lee, K.H.Yoo, Y.K.Kang, J.M.Kim, C.S.Lee, "A Study on Strawberry Harvesting Robot System Using YOLO-based Object Recognition," Journal of the Korean Society of Manufacturing Engineering, Vol.32, No.2, pp.101-108, 2023. https://doi.org/10.7735/ksmte.2023.32.2.101
  13. Data Portal[Internet], https://www.data.go.kr.
  14. YOLOv5, YOLOv5 by Ultralytics[Internet], https://github.com/ultralytics/yolov5.
  15. J.H.Moon, B.Peng, J.H.Kwon, T.K.Kim, "Implementation of Smart Umbrella Stand Based on IoT," Journal of Internet of Things and Convergence, Vol.9, No.1, pp.57-64, 2023. https://doi.org/10.20465/KIOTS.2023.9.1.057
  16. S.B.Park, Y.J.Jeong, D.E.Lee, T.K.Kim, "A Study on the Elevator System Using Real-time Object Detection Technology YOLOv5," Journal of Internet of Things and Convergence, Vol.10, No.2, pp.103-108, 2024. https://doi.org/10.20465/KIOTS.2024.10.2.103
  17. T.K.Kim, "Spatial Crowdedness Measurement System using IoT and Amazon Web Services," Journal of Internet of Things and Convergence, Vol.9, No.4, pp.15-20, 2023. https://doi.org/10.20465/KIOTS.2023.9.4.015
  18. R.B.Girshick, J.Donahue, T.Darrell, J.Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition, pp.580-587, 2014.
  19. R.B.Girshick, "Fast R-CNN," Proceedings of the IEEE international conference on computer vision, pp.1440-1448.
  20. S.Ren, K.He, R.B.Girshick, J.Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," Advances in neural information processing systems, 2015.