Online Public Transit Ridership Monitoring Through Passive WiFi Sensing

Abstract

Online public transit ridership information is helpful to enhance the service quality of urban public transportation and the travel experiences of passengers. Passive WiFi sensing collects WiFi probe (request) frames sent by nearby mobile devices in a non-intrusive manner, and can thus be employed to monitor ridership. Compared with the existing non-WiFi based approaches, passive WiFi sensing based approaches demonstrate the advantages of limited interferences, large coverage, low costs and lightweight calculations. More recently, although some dedicated passive WiFi sensing based methods have been proposed in an offline mode, due to sniffing opportunistically, unknown dynamic transmission boundary, MAC randomization and the difficulty in online feature extraction, how to utilize limited sensing data to provide accurate online ridership information is still challenging. To this end, an innovative public transit ridership monitoring system built upon a customized WiFi sniffer and an online ridership estimation algorithm is developed. In the algorithm, a convolutional neural network (CNN) module and a bidirectional long short-term memory (BiLSTM) neural network module are first adopted to find correlations among inputs and capture the bidirectional time-series features, respectively; furthermore, an attention module is incorporated to determine the importance of an input sequence at different times. Real-world experiments are carried out on 8 buses corresponding to 4 bus routes in Hohhot, China. The evaluation results show that the proposed algorithm outperforms the other 4 online algorithms and the state-of-the-art offline algorithm.