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

Paul Torrens, Xun Li, William Griffin

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

We introduce a novel scheme for automatically deriving synthetic walking (locomotion) and movement (steering and avoidance) behavior in simulation from simple trajectory samplesWe 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 schemeThis scheme produces movement behavior for non-sampled scenarios in simulation, for applications that can differ widely from the original collection settingsIt 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 clusteringThe scheme then weights, trains, and tunes likely synthetic movement behavior, per-agent, per-location, per-time-step, and per-scenarioTo 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 for traditional transition-driven methods for determining agent behaviorThe potential broader applications of the scheme are numerous and include the design and delivery of location-based 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

Original languageEnglish (US)
Pages (from-to)67-94
Number of pages28
JournalTransactions in GIS
Volume15
Issue numberSUPPL. 1
DOIs
StatePublished - Jul 2011

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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