Passenger Counting in Mass Public Transport Systems using Computer Vision and Deep Learning
Abstract
Estimating the number of people in vehicles and stations in public transport systems is crucial to improve service quality. The TransMilenio system in Bogota has serious drawbacks due to the lack of information in congestion situations. In this work we present a computer vision method that estimates the number of people in TransMilenio stations using deep learning techniques. We offer free use of the TransMilenio-Javeriana database with nearly 900,000 head labels on buses and stations. From these images a deep learning architecture tuned for crowd counting was trained to generate density maps around the heads in the scene. Several head count methods were evaluated on the density maps. After testing the method with 10,800 images, the results show a mean absolute error of 1 head per frame, equivalent to 11% relative error. The accuracy of this method is much better than its manual counterpart. This method is also scalable and low cost, which indicates that it has great potential to provide information for the planning and operation of public transport systems.
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Head count methods tested on density maps with 10,800 images show a mean absolute error of 1 head per frame, equivalent to 11% relative error.
“Head count methods were tested on density maps with 10,800 images [49]; the results show a mean absolute error of 1 head per frame, equivalent to 11% relative error.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [49]
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Pronello, C., & Garzón Ruiz, X. R. (2023) · Sensors · doi:10.3390/s23187719 · Cited by 24 · see citing works
Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24 · see citing works
Kalita, D., Talukdar, A. K., Deka, S., & Sarma, K. K. (2024) · IEEE International Conference on Computer Vision and Machine Intelligence (CVMI) · doi:10.1109/cvmi61877.2024.10782123 · Cited by 6 · see citing works
Sudhakaran, S., & Vadivelu, K. D. (2025) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2025.2559411
Enhancing Mobility in Smart Cities: People Counting and Characteristics Detection in Public Buses
Amirgaliyev, B., Mussabek, M., Zhumadillayeva, А., & Baishemirov, Z. D. (2025) · IEEE European Technology and Engineering Management Summit (E-TEMS) · doi:10.1109/e-tems64751.2025.11239138
- Authors
- Moreno Rendon, W. D., Burgos Anillo, C., Jaramillo-Ramirez, D., & Carrillo, H.
- Affiliations
- Pontificia Universidad Javeriana
- Pontificia Universidad Javeriana, Bogota, Colombia
- Universidade Federal de Ouro Preto (BR)
- Venue
- IEEE Latin America Transactions
- Published
- 2023-04
- Volume
- 21
- Issue
- 4
- Pages
- 537-545
- ISSN
- 1548-0992 (ISSN-L)
- OpenAlex
- W4376869745
- Cited by
- 5
- References
- 0
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 1
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Latin America Transactions (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- passenger counting, crowd counting, public transport, TransMilenio, bus terminal, head detection, density maps
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