Analysis of pedestrian behaviors through non-invasive Bluetooth monitoring

TL;DR

This study uses non-invasive Bluetooth detection to analyze pedestrian behavior patterns in Barcelona's historic shopping district, revealing increased exploration during sales periods through large-scale data analysis.

Contribution

It introduces a large-scale Bluetooth-based monitoring method to study pedestrian behaviors, providing insights into mobility patterns during sales periods.

Findings

Pedestrians explore wider areas during discount periods.

Bluetooth detection captures large-scale behavioral data.

Mobility patterns vary significantly before, during, and after sales.

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.