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

TL;DR

This study uses agent-based simulation to demonstrate that street network structure primarily determines human movement patterns, with little influence from individual movement behaviors, challenging traditional space syntax indicators.

Contribution

It reveals that street structure dominates movement flow, and proposes that PageRank-based measures outperform traditional centrality indicators in predicting aggregate human movement.

Findings

Street structure mainly shapes aggregate flow.

Random and purposive walkers produce similar movement patterns.

PageRank and related measures better predict movement than closeness centrality.

Abstract

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