Crowd-Flow Graph Construction and Identification with Spatio-Temporal Signal Feature Fusion

Abstract

To realize an efficient and flexible urban crowd-flow identification, we propose a new scheme called CFid by fusing fine-grained spatio-temporal smartphone signal features. Considering the abundance of ambient WiFi and geomagnetism, we design and validate several of their fine-grained features and similarity metrics to determine if two individuals belong to the same crowd-flow. We formalize a graph stream clustering problem, where co-flow user pairs are opportunistically identified and fed as a dynamic sequence of edges connecting each other. Given such a flexible form, a fast and accurate algorithm processes the edges and identifies the crowd-flows for further upper-level applications, including urban-flow monitoring and shopping-advertisement/recommendation. Using extensive data-driven analytics and 8-month experimental studies upon 50 users (over 2,500 walking traces at 7 different urban sites), we have validated the accuracy (usually >95%), efficiency (only <;5% extra energy-footprint on average than normal usage on mobiles) and flexibility of CFid in identifying large-scale crowd-flows.