Can cognitive inferences be made from aggregate traffic flow data?. (November 2015)

• Record Type: Journal Article
• Authors: Omer, Itzhak; Jiang, Bin
• Is Part Of: Computers, environment and urban systems. Volume 54(2015)
• Page Start: 219
• Page End: 229
• Publication Date: 2015-11
• Subjects: Urban movement -- Cognitive distance -- Network analysis -- Space syntax -- Agent-based simulation; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2015.08.005
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: 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. Highlights: Topological–angular movement potential is realized more fully than metric movement potential. Topological–angular network effects are dominant in the formation of movement patterns. Correlations between aggregate flow and street network centrality measures cannot be used for cognitive inferences.