'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
Betweenness centrality is an important measure in network sciences that reflects the extent a node lies in between any pairs in a graph. The measure has been used by urban studies, to discuss the relationship between urban mobility and the spatial street network of a city, using Dijkstra shortest path betweenness centrality to describe human wayfinding procedures. As in reality, wayfinding is a more complex endeavor, results of studies using both random path or the most optimal shortest path approach might be misleading. In this paper we propose with the exploratory betweenness centrality (EBC) a more realistic set of measures that uses an exploratory path in calculating centrality rather than an optimal path in studying pedestrian movement. In particular we calculate EBC where the agent selects the longest street nearest to the destination (App-EBC) or any random street that is approaching the destination (Ran-EBC). In doing so, we compare how EBC and GBC correlate with aggregate pedestrian movement for two case studies in London. The result shows the EBC measures explains equal or greater variation of aggregate pedestrian movement than the GBC measure for both of the case studies, indicating the potential of using measures of EBC in modeling urban mobility
