Technology Features & Specifications
The crowd simulation platform has the following major innovations: Crowd behaviors are derived directly from raw indoor movement traces, which are sparse, incorrect and noisy when compared to outdoor GPS sensing. To address this issue, we have developed and tested a novel trajectory mining algorithm that can combine a large number of “similar” trajectories to infer the likeliest routes for visitors visiting different parts of the building. Crowd simulation is 3D. To make our simulator easily adoptable, we have designed a unique set of tools that can create a 3D building model based on annotated 2D floorplans, with only minor manual modifications. This design significantly reduces the model building time. By utilizing machine-learning techniques together with the multivariate data (i.e. Wifi trace, CCTV camera videos), we propose a novel method to correlate the source and destination choices and the route choices of crowd to certain layout and motion features that are transferrable to different scenarios. With multiple datasets, the trained weights of such features can thus be applied to simulate and predict crowd movements in new scenarios. The complexity of configuring and setting up the simulation is hidden from the users with a user-friendly web portal. The crowd simulation platform enables venue operators to set up management and operation policy by altering infrastructure set up such as escalator directions, exit ways etc within the facility and assess crowd movement and congestion levels for specific events.
The primary application area of our technology is in crowd management within a large indoor facility. This may include but not limited to convention and meeting centers, sports stadium, or shopping mall. The strength of our crowd simulation platform is the ability in satisfying end-to-end modeling and usage needs: The crowd behaviors, which are of critical importance in correctly evaluating the management policy, are directly sensed and derived from the collected movement traces. Our platform can work even when the collected information is of inferior quality. The simulation, once equipped with learned crowd movement models, can also be easily operated using a user-friendly web portal. Through this portal, users can easily adjust parameters to be tested, set up simulation experiments, and collect and observe the generated outcomes.