TransitCrowd: Estimating Subway Stations Demand with Mobile Crowdsensing Data

Public Transport (PT) demand estimation relies on survey-based or, where available, smartcard passenger data. However, such data are rarely made available to researchers, if not in form of limited samples in time and space. An additional challenge is the variety of formats and the low granularity in which such data is available. Recently, first steps towards the use of advanced ICT-based data-driven approaches leveraging mobile crowdsensing technologies have started to emerge. These new data sources can provide new opportunities for generating more data and insights into transit demand patterns and behaviour. In this paper, we propose a novel data-driven transit demand estimation process, TransitCrowd, and apply it to subway stations. TransitCrowd estimates the passengers entering and exiting each station using as proxy the subway popularity index provided by Google Popular Times crowdsensed information, available at sheer scale in many cities. TransitCrowd’s key component is its one-time calibration process, which creates temporal signatures of the stations based on the difference between GPT information and entry-exit flow data, and regression-based machine learning and live GPT to estimate passenger flows in real time. We assess TransitCrowd’s estimation accuracy for two cities across a two-months period, i.e., New York and Washington DC, showing very promising results for both estimation and real-time prediction of transit flows at subway stations.