The large availability of urban surveillance video, combined with recent advances in deep learning, allows us to exploit them to support road safety by analyzing pedestrian behavior in real-time. This work proposes an XAI-driven approach for monitoring crosswalks with limited visibility, where the risk to Vulnerable Road Users (VRUs) is particularly high. We focus specifically on Limited-Mobility VRUs (LM-VRUs), including wheelchair users or adults with strollers, who may have a reduced ability to respond quickly to dangerous situations. The system processes live video streams from fixed surveillance cameras using deep learning-based computer vision to detect and track LM-VRUs in both crossing and waiting areas. Unlike data-intensive predictive models, our approach utilizes a lightweight, rule-based module that infers pedestrian crossing intent through human-understandable spatiotemporal heuristics. This explainable component ensures that the decision-making process remains transparent and auditable. Upon detecting a potentially dangerous crossing scenario, the system immediately activates acoustic and visual warnings for approaching drivers, improving safety, including for visually impaired pedestrians. Beyond its technical contribution, this work explores the social impact of AI technologies designed to protect mobility-impaired individuals in urban environments. Our goal is to reduce traffic-related accidents and contribute to more inclusive, intelligent city infrastructures.