CrowdTwin

CrowdTwin is a web + mobile digital twin for real-world venues (malls, airports, campuses, stadium districts) that forecasts foot-traffic density and queue risk in near real time. It ingests existing signals—Wi‑Fi association counts, BLE beacons, turnstiles, POS timestamps, camera analytics metadata (not raw video), and event schedules—to build a simple 2D/3D map overlay showing heat zones, predicted bottlenecks, and “what-if” scenarios (closing an entrance, adding staff, rerouting signage). The product is intentionally pragmatic: it won’t pretend to be a perfect physics simulation. Instead, it focuses on actionable operational decisions: staffing, cleaning, security posts, and tenant coordination. Alerts trigger when predicted density crosses thresholds, with recommended interventions and a post-event report that quantifies avoided congestion and service-level improvements. Setup is guided, with templates per venue type and a lightweight calibration period to learn baseline patterns.