An Experimental Study for Tracking Crowd in Smart Cities

Abstract

Knowledge about people density and mobility patterns is the key element toward efficient urban development in smart cities. The main challenges in large-scale people tracking are the recognition of people density in a specific area and tracking the people flow path. To address these challenges, we present SenseFlow, a lightweight people tracking system for smart cities. SenseFlow utilizes off-the-shelf sensors that sniff probe requests periodically polled by user's smartphones in a passive manner. We demonstrate the feasibility of SenseFlow by building a proof-of-concept prototype and undertaking extensive evaluations in real-world settings. We deploy the system in one laboratory to study office hours of researchers, a crowded public area in a city to evaluate the scalability and performance “in the wild,” and four classrooms in the university to monitor the number of students. We also evaluate SenseFlow with varying walking speeds and different models of smartphones to investigate the people flow tracking performance.

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