用多接收器Wi-Fi信号实现高精度乘客计数，适合公交调度场景。
RSSI-Assisted CSI-Based Passenger Counting with Multiple Wi-Fi Receivers
- 融合多接收器的信道状态与信号强度，边缘端协同计算
- 实测最高20人场景下准确率超94%，F1超94%
- 适合城市公交、地铁等需低成本高精度计数的场景
乘客计数对公共交通调度与交通容量评估至关重要。现有方法或成本高，或精度低，近年开始采用Wi-Fi信号解决此问题。本文提出一种基于边缘计算的乘客计数系统，包含多个Wi-Fi接收器和一个边缘服务器，利用信道状态信息（CSI）与接收信号强度指示（RSSI）实现多接收器协作。设计了一种新型特征融合模块——自适应RSSI加权的CSI特征拼接，将各接收器本地提取的CSI与RSSI特征在边缘服务器进行融合。在港岛双层巴士上采集的真实数据集上评估，最多容纳20名乘客，实验结果表明，系统平均准确率与F1分数均超过94%，较其他协作感知基线至少提升2.27%准确率与2.34% F1分数。
原文摘要 · Abstract (English)
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.
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