RSSI-Assisted CSI-Based Passenger Counting with Multiple Wi-Fi Receivers

TL;DR

This paper presents an efficient Wi-Fi-based passenger counting system using CSI and RSSI signals from multiple receivers, achieving over 94% accuracy in real-world tests on a bus.

Contribution

It introduces a novel adaptive RSSI-weighted CSI feature fusion method for improved passenger counting accuracy using multiple Wi-Fi receivers.

Findings

Achieves over 94% accuracy and F1-score in real-world bus environment.

Surpasses baseline methods by at least 2.27% in accuracy.

Demonstrates effective collaboration among multiple Wi-Fi receivers for passenger counting.

Abstract

Passenger counting is crucial for public transport vehicle scheduling and traffic capacity evaluation. However, most existing methods are either costly or with low counting accuracy, leading to the recent use of Wi-Fi signals for this purpose. In this paper, we develop an efficient edge computing-based passenger counting system consists of multiple Wi-Fi receivers and an edge server. It leverages channel state information (CSI) and received signal strength indicator (RSSI) to facilitate the collaboration among multiple receivers. Specifically, we design a novel CSI feature fusion module called Adaptive RSSI-weighted CSI Feature Concatenation, which integrates locally extracted CSI and RSSI features from multiple receivers for information fusion at the edge server. Performance of our proposed system is evaluated using a real-world dataset collected from a double-decker bus in Hong Kong, with up to 20 passengers. The experimental results reveal that our system achieves an average accuracy and F1-score of over 94%, surpassing other cooperative sensing baselines by at least 2.27% in accuracy and 2.34% in F1-score.