Outdoor Crowd Flow Estimation Using RSRP from Commercial LTE Base Station: A Field Study

TL;DR

This study demonstrates the feasibility of estimating outdoor crowd flow using RSRP data from commercial LTE base stations, employing machine learning to analyze the relationship between RSRP variance and pedestrian counts.

Contribution

It is the first to quantitatively validate outdoor crowd flow estimation using commercial LTE signals and analyzes the impact of feature design choices.

Findings

Optimal RSRP variance window size is 0.1 to 0.2 seconds.

Enlarging the counting area increases RSRP variance features.

Machine learning effectively correlates RSRP variance with pedestrian counts.

Abstract

With the advent of the 6G era, Integrated Sensing and Communications (ISAC) has attracted increasing attention. One representative of use cases is crowd flow estimation on outdoor streets. However, most existing studies have focused on indoor environments or vehicles, and demonstrations of outdoor crowd flow estimation using commercial LTE base station remain limited. This study addresses this use case and proposes an analysis of a crowd flow estimation method using Reference Signal Received Power (RSRP) obtained from a commercial LTE base station. Specifically, pedestrian counts derived from a camera-based object recognition algorithm were associated with the variance of RSRP. The features obtained from the variance were quantitatively evaluated by combining a CatBoost regression model with SHapley Additive exPlanations (SHAP) analysis. Through this investigation, we clarified that an optimal variance window size for RSRP is 0.1 to 0.2 seconds and that enlarging the counting area increased the features obtained from the variance of RSRP, for machine learning. Consequently, this study is the first to quantitatively demonstrate the effectiveness of outdoor crowd flow estimation using commercial LTE, while also revealing the characteristic behavior of variance window size and counting area size in feature design.