Building Agent-Based Walking Models by Machine-Learning on Diverse Databases of Space-Time Trajectory Samples

We introduce a novel scheme for automatically deriving synthetic walking (locomotion) and movement (steering and avoidance) behavior in simulation from simple trajectory samples. We use a combination of observed and recorded real-world movement trajectory samples in conjunction with synthetic, agent-generated, movement as inputs to a machine-learning scheme. This scheme produces movement behavior for non-sampled scenarios in simulation, for applications that can differ widely from the original collection settings. It does this by benchmarking a simulated pedestrian’s relative behavioral geography, local physical environment, and neighboring agent-pedestrians; using spatial analysis, spatial data access, classification, and clustering. The scheme then weights, trains, and tunes likely synthetic movement behavior, per-agent, per-location, per-time-step, and per-scenario. To prove its usefulness, we demonstrate the task of generating synthetic, non-sampled, agent-based pedestrian movement in simulated urban environments, where the scheme proves to be a useful substitute to traditional transition-driven methods for determining agent behavior. The potential broader applications of the scheme are numerous and include the design and delivery of locationbased services, evaluation of architectures for mobile communications technologies, what-if experimentation in agent-based models with hypotheses that are informed or translated from data, and the construction of algorithms for extracting and annotating space-time paths in massive data-sets.