Journal article
Predicting mobile users' next location using the semantically enriched geo-embedding model and the multilayer attention mechanism
Computers, environment and urban systems, Vol.104, p.102009
09/01/2023
DOI: 10.1016/j.compenvurbsys.2023.102009
Abstract
Predicting the next location of human mobility and its semantic information can support recommendations for location-based services and trajectory mining, such as human mobility pattern recognition and sequential anomaly detection. Previous studies have ignored the implicit correlation between location and spatiotemporal information thereby limiting the model performance in terms of location prediction accuracy. In this study, we propose a GEMA-BiLSTM (Geographical Embedding and Multilayer Attention-Bidirectional Long Short-Term Memory) model to predict the next-location in users' mobility. The model combines location and spatiotem-poral information to extract the semantics of human mobility. The results show that the model can accurately predict the next location with a high accuracy of 87.63%. Compared with BiLSTM-CNN, LSTM, CNN, and Markov, the location prediction accuracy of the model improved by 2.28%, 9.72%, 11.53%, and 17.64%, respectively. In addition, the model has the highest semantic prediction accuracy (75.35%). Compared with the BiLSTM-CNN model, the our method improves the semantic prediction accuracy for residential and industrial function areas by 4.79% and 5.37%, respectively. The accuracy of location prediction for different time periods indicates that the next location of human activity during morning rush and evening rush hours is the most difficult to predict, which corresponds to the increase in human travel demand. Moreover, weekday human activity patterns indicate that the commercial area is still very active at night, which may be linked to nighttime economic policies. This study could improve the accuracy of recommendations for location-based service applications.
Details
- Title: Subtitle
- Predicting mobile users' next location using the semantically enriched geo-embedding model and the multilayer attention mechanism
- Creators
- Yao Yao - China University of GeosciencesZijin Guo - Tokyo University of Information SciencesChen Dou - Wuhan UniversityMinghui Jia - Wuhan UniversityYe Hong - ETH ZurichQingfeng Guan - China University of GeosciencesPeng Luo - Technical University of Munich
- Resource Type
- Journal article
- Publication Details
- Computers, environment and urban systems, Vol.104, p.102009
- DOI
- 10.1016/j.compenvurbsys.2023.102009
- ISSN
- 0198-9715
- eISSN
- 1873-7587
- Publisher
- Elsevier
- Number of pages
- 14
- Grant note
- 20228670 / Alibaba Group through Alibaba Innovation Research Program 42171466; 41801306 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) 2022034 / The "CUG Scholar" Scientific Research Funds at China University of Geosciences (Wuhan) 2019YFB2102903 / National Key Research and Development Program of China; National Key Research & Development Program of China; National Key Technology R&D Program 202208440090 / China Scholarship Council
- Language
- English
- Date published
- 09/01/2023
- Academic Unit
- School of Earth, Environment, and Sustainability
- Record Identifier
- 9985219300702771
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