Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale

Abstract

Modelling and forecasting citywide crowd information (e.g., crowd volume of a region, the inflow of crowds into a region, outflow of crowds from a region) at a fine spatio-temporal scale is crucial for urban and transport planning, city management, public safety, and traffic management. However, this is a challenging task due to its complex spatial and temporal dependences. This paper proposes an effective and efficient model to reduce the training time cost while maintaining predictive accuracy in forecasting citywide crowd information at a fine spatio-temporal scale. Our model integrates Gated Recurrent Unit (GRU), convolutional neural network (CNN), and k-nearest neighbors (k-NN) to jointly capture the spatial and temporal dependences between two regions in a city. The evaluation with two different datasets in two different cities shows that compared to the state-of-the-art baselines, our model has better predictive accuracy (reducing the mean absolute errors MAEs by 20.99% on average) and a lower training time cost (reducing the time cost to only 26.16% on average of that of the baselines). Our model also has better abilities in making accurate predictions with low time cost under the influences of large-scale special events (when massive crowds of people are gathering in a short time) and for regions with high and irregular crowd changes. In summary, our model is an effective, efficient, and reliable method for forecasting citywide crowd information at a fine spatio-temporal scale, and has a high potential for many applications, such as city management, public safety, and transportation.