Human mobility information is widely needed by many sectors in smart cities, especially for public transport. This work designs lightweight algorithms to perform stop detection, passenger flow tracking, and passenger estimation in an automated train. The on-board learning and detection algorithms are running on an edge node that is integrated with Wi-Fi sniffing technology, a GPS sensor, and an inertial measurement unit (IMU) sensor. The stop detection algorithm determines if the automated train is static at which train station based on GPS and IMU data. When the train is moving, the correlations between statistical properties extracted from Wi-Fi probes and the actual number of passengers change. Therefore, two algorithms, passenger flow tracking and passenger estimation, are designed to analyze passenger mobility. The passenger flow tracking algorithm analyzes the number of incoming and outgoing passengers in the train. The passenger estimation algorithm approximates the number of passengers inside the train based on a multi-dimensional regression model created by statistical properties extracted from different device brands. The designed prototype is deployed in an automated hanging train to conduct real-world experiments. The experimental results indicate that the proposed passenger flow tracking algorithm reduces the average errors of 62.5% and 70% compared against two existing clustering algorithms respectively. When the devices' brands are split for creating a regression model, compared with two counting-based approaches, the proposed passenger estimation algorithm results in lower errors with a mean of 3.15 and a variance of 9.29.