2025/11/29 by Nthenya Kyatha, Jay Taneja, Kyatha, Nthenya +1
Social Sciences · Engineering · Computer Science · #Human Mobility and Location-Based Analysis #Traffic Prediction and Management Techniques #ICT in Developing Communities
paper · pdf · doi:10.48550/arxiv.2512.00424
Public transport in sub-Saharan Africa (SSA) often operates in overcrowded conditions where existing automated systems fail to capture reliable passenger flow data. Leveraging onboard CCTV already deployed for security, we present a baseline pipeline that combines YOLOv12 detection, BotSORT tracking, OSNet embeddings, OCR-based timestamping, and telematics-based stop classification to recover bus origin--destination (OD) flows. On annotated CCTV segments from Nairobi and Kigali buses, the system attains high counting accuracy under low-density, well-lit conditions (recall ≈95%, precision ≈91%, F1 ≈93%). It produces OD matrices that closely match manual tallies. Under realistic stressors such as overcrowding, color-to-monochrome shifts, posture variation, and non-standard door use, performance degrades sharply (e.g., ∼40% undercount in peak-hour boarding and a ∼17 percentage-point drop in recall for monochrome segments), revealing deployment-specific failure modes and motivating more robust, deployment-focused Re-ID methods for SSA transit.