<![CDATA[Purpose – This study develops and evaluates a low-cost Vision-AI-based roadside unit simulator integrated with SUMO to support adaptive traffic signal control and safer crossing for visually impaired pedestrians. Design/methods/approach – The proposed system combines a Raspberry Pi 4 edge platform, a camera sensor, YOLOv5s-based vehicle detection, and SUMO traffic simulation through the TraCI interface. Three control strategies were compared: fixed-time control, SUMO adaptive control, and the proposed Vision-AI-assisted control. Experiments were conducted under low, medium, and high traffic density scenarios, with 30 simulation runs for each condition. Performance was measured using average waiting time, queue length, travel time, pedestrian crossing success rate, detection accuracy, latency, and statistical significance testing. Findings - The proposed system outperformed both baseline methods across all scenarios. It reduced vehicle waiting time by up to 41%, queue length by approximately 35%, and travel time by around 22% compared with fixed-time control. The assistive crossing mechanism increased pedestrian crossing success from 62% to 93%. The edge platform achieved 18–22 FPS, latency below 85 ms, and mAP@0.5 of 0.87. Research implications/limitations – The findings demonstrate the feasibility of low-cost edge-based intelligent transportation systems, although validation remains limited to simulation and a single-intersection case. Originality/value – This study integrates Vision-AI traffic perception, adaptive signal control, and accessibility-aware pedestrian support within one simulation framework.]]>
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