Traffic analysis zones-based resource allocation analysis using Uber Movement data

Abstract

Effective resource allocation helps in alleviating traffic congestion. Making a homogeneous assumption that resource requirement scales only with land area, we propose a resource allocation scheme that uses centrality measurements derived from Metro Manila Uber Movement data. The Hoshen-Kopelman (HK) algorithm is applied to centrality maps generated from a previous work to obtain the total land area and largest cluster area values at various centrality thresholds. Our scheme allows us to tackle the question of prioritization should resources suddenly become limited.