Short-Term Estimation and Prediction of Pedestrian Density in Urban Hot Spots Based on Mobile Phone Data

Short-term estimation and prediction of pedestrian density in urban hot spots (e.g., railway station, shopping mall, etc.) is an important topic for traffic management and control in densely populated areas. In this paper, we propose a short-term pedestrian density estimation and prediction method based on mobile phone data. Firstly, pedestrian density in hot spots is estimated using mobile phone data. To decrease the positioning errors of mobile phone data, a modified particle filter method, which considers the movements of pedestrians, is applied for pre-processing the data. An efficient spatial access method (i.e., Hilbert R-tree) is adopted to construct pedestrians' position indexes for realizing the short-term estimation. Secondly, based on the estimation results, the spatiotemporal extended Kalman filter (SEKF) is proposed for the short-term prediction of pedestrian density. A massive mobile phone dataset collected in Nanjing, China is used in the case study. The estimated pedestrian density from Monday to Thursday is used for pedestrian density prediction on Friday. The results show that the proposed method can estimate and predict pedestrian density in hot spots, especially in small-scale sites of hot spots efficiently in a short time. Comparing with classical prediction methods, the proposed SEKF method predicts short-term pedestrian density in urban hot spots more accurately.