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Relying on random and purposive moving agents, we simulated human movement inlarge street networks. We found that aggregate flow, assigned to individualstreets, is mainly shaped by the underlying street structure, and that humanmoving behavior (either random or purposive) has little effect on the aggregateflow. This finding implies that given a street network, the movement patternsgenerated by purposive walkers (mostly human beings) and by random walkers arethe same. Based on the simulation and correlation analysis, we further foundthat the closeness centrality is not a good indicator for human movement, incontrast to a long standing view held by space syntax researchers. Instead wesuggest that Google's PageRank, and its modified version - weighted PageRank,betweenness and degree centralities are all better indicators for predictingaggregate flow.

Agent-based Simulation of Human Movement Shaped by the Underlying Street Structure