STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction
This work addresses the challenge of accurate and efficient human mobility forecasting for transportation and public safety, representing an incremental improvement over existing methods.
The paper tackles the problem of predicting citywide human mobility by proposing STAR, a spatio-temporal framework based on a fully-convolutional residual network, which outperforms state-of-the-art methods in accuracy and efficiency with fewer parameters.
Human mobility forecasting in a city is of utmost importance to transportation and public safety, but with the process of urbanization and the generation of big data, intensive computing and determination of mobility pattern have become challenging. This study focuses on how to improve the accuracy and efficiency of predicting citywide human mobility via a simpler solution. A spatio-temporal mobility event prediction framework based on a single fully-convolutional residual network (STAR) is proposed. STAR is a highly simple, general and effective method for learning a single tensor representing the mobility event. Residual learning is utilized for training the deep network to derive the detailed result for scenarios of citywide prediction. Extensive benchmark evaluation results on real-world data demonstrate that STAR outperforms state-of-the-art approaches in single- and multi-step prediction while utilizing fewer parameters and achieving higher efficiency.