Can we learn where people go?
This addresses the challenge of obtaining essential destination parameters for agent-based simulators in pedestrian modeling, but it is incremental as it builds on existing simulation methods.
The paper tackled the problem of predicting pedestrian destination distributions from video data, using density heatmaps as input and a Random Forest predictor on simulated crossroad data, achieving a proof of concept that motivates further analysis.
In most agent-based simulators, pedestrians navigate from origins to destinations. Consequently, destinations are essential input parameters to the simulation. While many other relevant parameters as positions, speeds and densities can be obtained from sensors, like cameras, destinations cannot be observed directly. Our research question is: Can we obtain this information from video data using machine learning methods? We use density heatmaps, which indicate the pedestrian density within a given camera cutout, as input to predict the destination distributions. For our proof of concept, we train a Random Forest predictor on an exemplary data set generated with the Vadere microscopic simulator. The scenario is a crossroad where pedestrians can head left, straight or right. In addition, we gain first insights on suitable placement of the camera. The results motivate an in-depth analysis of the methodology.