CrowdMeter: Congestion Level Estimation in Railway Stations Using Smartphones

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 context (e.g., waiting for a train, buying a ticket) along 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. Finally, the system highlights each area of the station with a specific color (green, amber, red) that corresponds to one of a three congestion levels (low, medium, high).Evaluation of CrowdMeter through a field experiment in 10 different train stations in Japan shows that it can infer the congestion levels accurately, highlighting its promise as a ubiquitous travel-support service.

I. Introduction

In highly populated cities, most commuters (e.g., 79% in Tokyo [15]) use trains, causing the traffic volume on certain trains to be so intense (e.g., cars capacity reaches 250% on average during rush hours compared to their typical capacity [46]). Even worse, train doors in developing countries never close in some lines, killing a commuter every day [13]. Accordingly, overcrowding creates high levels of discomfort making train riding harder for many passengers (e.g., pregnant women, handicapped people and parents with infants). Thus, many travelers may decide their preferable routes based on the potential comfort level rather than the predicted travel time [19], [27].