Long-time gap crowd prediction using time series deep learning models with two-dimensional single attribute inputs

• Published in: Advanced engineering informatics 2022-01, Vol.51, p.101482, Article 101482
• Main Authors: Poon, Kwok Ho, Wong, Peter Kok-Yiu, Cheng, Jack C.P.
• Format: Article
• Language: English
• Subjects: Crowd prediction; Deep learning; Long-time gap prediction; Multivariate inputs; Time series analysis
• ISSN: 1474-0346; 1873-5320
• DOI: 10.1016/j.aei.2021.101482

•Proposed time-series method for crowd prediction with long-time gap (1 day ahead).•Proposed 2D inputs that exploit prior patterns over past days for improved prediction.•Conducted sensitivity analyses of time gap length and input feature size on prediction accuracy.•The LT2D-method improves different baseline models generally by 22% in accuracy. Crowd prediction is a crucial aspect of modern life with innumerable applications. By predicting future human occupancy in advance, crowd prediction can support the decision-making processes of facility stakeholders, e.g., the campus operator can schedule facility maintenance during the period of lowest pedestrian flow to eliminate any disturbance. Conventional crowd prediction utilizes statistical models and rule-based data mining techniques, which are tedious in data processing and error-prone. Hence, this study formulates crowd prediction into a time-series analysis based on deep learning. Despite its wide adaptability in various research fields, deep learning-based time series analysis is seldom adopted in crowd prediction. There are two major limitations in previous studies: firstly, the prediction accuracy notably degrades with increased prediction length, and secondly only the temporal pattern along a single time dimension is exploited, i.e., the consecutive time steps in the most recent input data. Therefore, a Long-Time Gap Two-Dimensional method, entitled LT2D-method, is proposed to increase the crowd prediction length of with high accuracy. The LT2D-method is composed of two parts, (1) long-time gap prediction, which extends the prediction length to 240 time steps (1 day) with high accuracy, and (2) 2D inputs method, which exploits the prior knowledge from different time dimensions to further improve the prediction accuracy of long-time gap prediction. The proposed LT2D-method can be generally adapted to deep learning models, such as LSTM, BiLSTM, and GRU, to improve the prediction accuracy. By incorporating the proposed LT2D-method into different baseline models, the accuracy is generally improved by around 22%, demonstrating the robustness and generalizability of our method.