Parameterizing a Pedestrian Agent-Based Model Using an Online Game

Abstract

Agent-based models (ABMs) are often parameterized using empirical data from the real world.  For some ABMs this is not possible because the reality upon which the models are based does not exist or is not generalizable from one setting to another.  In this paper we implement an online decision game to parameterize an agent-based model of pedestrian route choice decisions in a neighbourhood.  Our conceptual framework is to use an experimental game to log decision-making behaviour, summarize this behaviour into a decision model, and then transfer this model to an ABM.  The product of this framework is an ABM with agents informed by human decision making made within the game, rather than the real world.  The results of our analysis suggest that the decision model is consistent with some general theory about pedestrian decision making, but the ABM illustrates some unique and contextually specific patterns of pedestrian flow.  ABMs parameterized with game data may be useful for forecasting the effects of change on urban transportation infrastructure.

Keywords: research gaming, travel safety, online data collection