Analysis of pedestrian behaviors through non-invasive Bluetooth monitoring

Abstract

This paper analyzes pedestrians' behavioral patterns in the pedestrianized shopping environment in the historical center of Barcelona, Spain. We employ a Bluetooth detection technique to capture a large-scale dataset of pedestrians' behavior over a one-month period, including during a key sales period. We focused on comparing particular behaviors before, during, and after the discount sales by analyzing this large-scale dataset, which is different but complementary to the conventionally used small-scale samples. Our results uncover pedestrians actively exploring a wider area of the district during a discount period compared to weekdays, giving rise to strong underlying mobility patterns.