Can cognitive inferences be made from aggregate traffic flow data?

Space syntax analysis or the topological analysis of street networks has illustrated that human traffic flow is highly correlated with some topological centrality measures, implying that human movement at an aggregate level is primarily shaped by the underlying topological structure of street networks. However, this high correlation does not imply that any individual's movement can be predicted by any street network centrality measure. In other words, traffic flow at the aggregate level cannot be used to make inferences about an individual's spatial cognition or conceptualization of space. Based on a set of agent-based simulations using three types of moving agents - topological, angular, and metric - we show that topological-angular centrality measures correlate better than does the metric centrality measure with the aggregate flows of agents who choose the shortest angular, topological or metric routes. We relate the superiority of the topological-angular network effects to the structural relations holding between street network to-movement and through-movement potentials. The study findings indicate that correlations between aggregate flow and street network centrality measures cannot be used to infer knowledge about individuals' spatial cognition during urban movement.