Online Spatio-Temporal Crowd Flow Distribution Prediction for Complex Metro System
| Autor: | Yongshun Gong, Wei Liu, Jian Zhang, Yu Zheng, Zhibin Li |
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| Rok vydání: | 2022 |
| Předmět: |
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Operations research Computer science business.industry media_common.quotation_subject 02 engineering and technology Space (commercial competition) Computational fluid dynamics Computer Science Applications Computational Theory and Mathematics Flow (mathematics) 020204 information systems 0202 electrical engineering electronic engineering information engineering Key (cryptography) Quality (business) 08 Information and Computing Sciences Focus (optics) business Intelligent transportation system Information Systems media_common |
| Zdroj: | IEEE Transactions on Knowledge and Data Engineering. 34:865-880 |
| ISSN: | 2326-3865 1041-4347 |
| Popis: | As a key mission of the modern traffic management, crowd flow prediction (CFP) benefits in many tasks of intelligent transportation services. However, most existing techniques focus solely on forecasting entrance and exit flows of metro stations that do not provide enough useful knowledge for traffic management. In practical applications, managers desperately want to solve the problem of getting the potential passenger distributions to help authorities improve transport services, termed as crowd flow distribution (CFD) forecasts. Therefore, to improve the quality of transportation services, we proposed three spatiotemporal models to effectively address the network-wide CFD prediction problem based on the online latent space (OLS) strategy. Our models take into account the various trending patterns and climate influences, as well as the inherent similarities among different stations that are able to predict both CFD and entrance and exit flows precisely. In our online systems, a sequence of CFD snapshots is used as the training data. The latent attribute evolutions of different metro stations can be learned from the previous trend and do the next prediction based on the transition patterns. All the empirical results demonstrate that the three developed models outperform all the other state-of-the-art approaches on three large-scale real-world datasets. |
| Databáze: | OpenAIRE |
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