Analyzing the needs and expectations of Ho Chi Minh City residents regarding AI-enabled cameras
Published: September 16, 2026
Abstract
This study analyzes the needs and expectations of Ho Chi Minh City residents regarding AI-enabled cameras for bus passenger-flow management. Data comprise 356 responses from adults aged 18 and above. Four multiple-response questions examined bus management problems, desired camera and counting-system functions, reasons for supporting the solution, and expected service improvements. Frequencies, valid-response rates, Chi-square tests, Cramér’s V, Spearman correlation, and Benjamini-Hochberg FDR correction were applied. Peak-hour overcrowding was the most frequently identified problem (64.6%), safety alerts the most desired function (58.4%), improved management and dispatching the leading reason for support (60.6%), and better bus allocation to prevent overcrowding the most expected improvement (62.8%). Gender had a weak association with bus-use frequency, while age showed no significant association. Seven items remained significantly different across age groups after FDR correction.
Keywords
AI-enabled camerapassenger flowbus transportuser needspublic transportHo Chi Minh City
References
1.
Hội đồng nhân dân Thành phố Hồ Chí Minh (2026). Xe buýt miễn phí mở đường cho tương lai xanh của Thành phố Hồ Chí Minh. https://hdnd.hochiminhcity.gov.vn/tin-tuc/tin-tong-hop/xe-buyt-mien-phi-mo-duong-cho-tuong-lai-xanh-cua-tp-hcm.
2.
Quốc hội (2025). Luật Bảo vệ dữ liệu cá nhân số 91/2025/QH15, ban hành ngày 26/6/2025, hiệu lực từ ngày 01/01/2026.
3.
Tiến Lực (2026). Thành phố Hồ Chí Minh chủ trương miễn phí xe buýt cho người dân. Cổng thông tin điện tử Hội đồng nhân dân Thành phố Hồ Chí Minh. https://hdnd.hochiminhcity.gov.vn/tin-tuc/tin-tong-hop/thanh-pho-ho-chi-minh-chu-truong-mien-phi-xe-buyt-cho-nguoi-dan.
4.
Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57, 289-300.
5.
Cats, O., Susilo, Y. O. and Reimal, T. (2017). The prospects of fare-free public transport: evidence from Tallinn. Transportation, 44(5), 1083-1104.
6.
Hsu, Y. W., Chen, Y. W. and Perng, J. W. (2020). Estimation of the number of passengers in a bus using deep learning. Sensors, 20(8), 2178.
7.
Pronello, C., Anbarasan, D., Spoturno, F. et al. (2025). A low-cost automatic people-counting system at bus stops using Wi-Fi probe requests and deep learning. Public Transport, 17, 71-100.
8.
Radovan, A., Mršić, L., Dambić, G. and Mihaljević, B. (2024). A review of passenger counting in public transport concepts with solution proposal based on image processing and machine learning. Eng, 5(4), 3284-3315. https://doi.org/10.3390/eng5040172.
