The fast pace of urbanization has given rise to complex transportation networks, such as subway systems, that deploy smart card readers generating detailed transactions of mobility. Predictions of human movement based on these transaction streams represents tremendous new opportunities from optimizing fleet allocation of on-demand transportation such as UBER and LYFT to dynamic pricing of services. However, transportation research thus far has primarily focused on tackling other challenges from traffic congestion to network capacity. To take on this new opportunity, we propose a real-time framework, called PULSE (Prediction Framework For Usage Load on Subway SystEms), that offers accurate multi-granular arrival crowd flow prediction at subway stations. PULSE extracts and employs two types of features such as streaming features and station profile features. Streaming features are time-variant features including time, weather, and historical traffic at subway stations (as time-series of arrival/departure streams), where station profile features capture the time-invariant unique characteristics of stations, including each station's peak hour crowd flow, remoteness from the downtown area, and mean flow. Then, given a future prediction interval, we design novel stream feature selection and model selection algorithms to select the most appropriate machine learning models for each target station and tune that model by choosing an optimal subset of stream traffic features from other stations. We evaluate our PULSE framework using real transaction data of 11 million passengers from a subway system in Shenzhen, China. The results demonstrate that PULSE greatly improves the accuracy of predictions at all subway stations by up to 49 % over baseline algorithms.
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