Review and Selection of Methods for Automated Passenger Counting on Public Land Transport for Effective Transportation Management
https://doi.org/10.26518/2071-7296-2025-22-2-238-247
EDN: PPKZFC
Abstract
Introduction. The study aims to analyze modern automatic passenger counting methods in public transport. The study addresses the pressing issue of passenger flow counting in public transport using modern technologies such as video surveillance, infrared sensors, and LiDAR.
Materials and Methods. An overview of technologies is provided, including sensors, cameras, LiDAR, and RFID, along with analysis methods based on theoretical and empirical approaches. Information from development companies is used to compare the accuracy of technologies in real-world conditions.
Results. The comparison results indicate that LiDAR and cameras with machine learning offer the highest accuracy, particularly in high passenger density scenarios. Wi-Fi and Bluetooth-based technologies have limited accuracy, but combined solutions can overcome their drawbacks.
Discussions and Conclusions. The conclusion emphasizes that LiDAR and video surveillance with machine learning are the most effective for accurate passenger counting. Further testing of combined technologies and the development of flexible systems are recommended, along with innovative approaches in neural network training to enhance accuracy.
About the Authors
Andrei D. Plakhtii
St. Petersburg State University
Russian Federation
Russian Federation
Plakhtii Andrei D. – Postgraduate Student,
7-9, Universitetskaya emb., St. Petersburg, 199034.
Denis S. Korchagin
Federal State Budgetary Educational Institution of Higher Education “Saint Petersburg State University of Architecture and Civil Engineering”; Modern Technologies LLC
Russian Federation
Russian Federation
Korchagin Denis S. – Postgraduate Student; CEO,
4, 2nd Krasnoarmeiskaya Str., St Petersburg, 190005;
Nr.18, Liter A, Office 414A, Khrustalnaya Street, St Petersburg, 192019.
References
1. Kuipers, R. A., & Palmqvist, C.-W., “Passenger Volumes and Dwell Times for Commuter Trains: A Case Study Using Automatic Passenger Count Data in Stockholm”. Appl. Sci. 2022; vol. 12, no. 12: 5983, Jun. 2022. https://doi.org/10.3390/app12125983.
2. Andreev K.P. The Improvement of Transport Services of the Population. Transport Business in Russia. 2017; 3: 7–9. (In Russ.)
3. Petrova D.V. Modern Approaches to the Organization of Public Transport Passenger Traffic Monitoring in Urban Agglomerations. International Journal of Open Information Technologies. 2020; Vol. 8, No. 1: 47–57 (In Russ.)
4. Kovalev A.M., Egorov K.V., Sanzhapov R.R., Prytkova E.G. Testing of the system of hardware accounting of passenger traffic in real conditions of the city route. Technical and technological problems of the service. 2021; 4 (58): 12–18 (In Russ.)
5. Grgurević I., Juršić K., Rajič V. Review of Automatic Passenger Counting Systems in Public Urban Transport. 5th EAI International Conference on Management of Manufacturing Systems. Cham: Springer. 2022: 1–15. https://doi.org/10.1007/978-3-030-67241-6_1.
6. Kotz A., Kittelson D.B., Northrop W.F. Novel Vehicle Mass-Based Automated Passenger Counter for Transit Applications. Transp. Res. Rec. 2016. Т. 2536. https://doi.org/10.3141/2536-05.
7. Nitti M., Pinna F., Pintor L., et al. iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System. Energies. 2020; Т. 13, no 6. https://doi.org/10.3390/en13061446.
8. Jiang H., Chen S., Xiao Z., et al. Pa-Count: Passenger Counting in Vehicles Using Wi-Fi Signals. IEEE Trans. Mob. Comput. 2024; Т. 23, no 4: 2684–2697. https://doi.org/10.1109/TMC.2023.3263229.
9. Khan S., Yousaf M.H., Murtaza F., Velastin S. Passenger Detection and Counting for Public Transport System. NED Univ. J. Res. 2020; Т. XVII: 35–46. https://doi.org/10.35453/NEDJR-ASCN-2019-0016.
10. Labit-Bonis C., Thomas J., Lerasle F. Visual and Automatic Bus Passenger Counting Based on a Deep Tracking-by-Detection System. Available at: https://hal.science/hal-03363502 (acceded: 30.10.2024)
11. Seidel R., Jahn N., Seo S., et al. NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset. IEEE Open J. Intell. Transp. Syst. 2022; Т. 3: 33–44. https://doi.org/10.1109/OJITS.2021.3139393.
12. Pronello C., Ruiz X.R.G. Evaluating the Performance of Video-Based Automated Passenger Counting Systems in Real-World Conditions: A Comparative Study. Sensors. 2023; Т. 23, no 18. https://doi.org/10.3390/s23187719
13. Hsu Y.-W., Wang T.-Y., Perng J.-W. Passenger Flow Counting in Buses Based on Deep Learning Using Surveillance Video. Optik. 2020; Т. 202. https://doi.org/10.1016/j.ijleo.2019.163675.
14. Ge L., Sarhani M., Voß S., Xie L. Review of Transit Data Sources: Potentials, Challenges and Complementarity. Sustainability. 2021; Т. 13, no 20^ 11450. https://doi.org/10.3390/su132011450.
15. Zhang J., et al. A Real-Time Passenger Flow Estimation and Prediction Method for Urban Bus Transit Systems. IEEE Trans. Intell. Transp. Syst. 2017; Т. 18, no 11. https://doi.org/10.1109/tits.2017.2686876.
16. Demissie M.G., et al. Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal. IEEE Trans. Intell. Transp. Syst. 2016; Т. 17, no 9: 2466–2478. https://doi.org/10.1109/TITS.2016.2521830.
17. Mccarthy C., et al. A Field Study of Internet of Things-Based Solutions for Automatic Passenger Counting. IEEE Open J. Intell. Transp. Syst. 2021; Т. 2: 384–401. https://doi.org/10.1109/OJITS.2021.3111052.
18. Kuchár P., Pirník R., Janota A., et al. Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges. Sustainability. 2023; Т. 15, no 2: 1332. https://doi.org/10.3390/su15021332.
Review
For citations:
Plakhtii A.D., Korchagin D.S. Review and Selection of Methods for Automated Passenger Counting on Public Land Transport for Effective Transportation Management. The Russian Automobile and Highway Industry Journal. 2025;22(2):238-247. (In Russ.) https://doi.org/10.26518/2071-7296-2025-22-2-238-247. EDN: PPKZFC
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