© 2019 Elsevier B.V. We present CrowdMeter: a participatory system that leverages the sensed data collected from users’ phones during their daily train commutes to gauge the real-time congestion level in railway stations. CrowdMeter tracks the passenger's position in the station as well as identifies her/his context (e.g., waiting for a train, buying a ticket) along with her trajectory from the station's entrance to the train. Therefrom, CrowdMeter extracts novel features, based on the user's location and context, from the phone sensors. These features capture the passenger's behavior (e.g., the walking pattern) and the ambient environment characteristics (e.g., the ambient sound) that can indicate the surrounding congestion level along the passenger's route in a railway station. CrowdMeter utilizes the passengers’ contexts to show the congestion level for each area such as crowd density in passageways and the queue length of ticketing machines. Both passengers and operators can easily recognize the more and less congested areas, which helps to support proper decision making in their trips and smarter guidance of crowds. Evaluation of CrowdMeter through a field experiment in 29 different train stations in Japan shows that it can infer the congestion levels accurately, highlighting its promise as a ubiquitous travel-support service.