Predicting Personal Next Location Based on Stay Point Feature Extraction

LI Fan; XIA Jizhe; HUANG Zhao; LI Xiaoming; LI Qingquan

doi:10.13203/j.whugis20200068

Volume 45 Issue 12

Dec. 2020

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Geomatics and Information Science of Wuhan University > 2020 > 45(12): 1970-1980. > DOI: 10.13203/j.whugis20200068

LI Fan, XIA Jizhe, HUANG Zhao, LI Xiaoming, LI Qingquan. Predicting Personal Next Location Based on Stay Point Feature Extraction[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1970-1980. DOI: 10.13203/j.whugis20200068

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Predicting Personal Next Location Based on Stay Point Feature Extraction

LI Fan^{1, 5,},
XIA Jizhe^{1, 2, 3, 4, ,},
HUANG Zhao¹,
LI Xiaoming^{2, 3, 4},
LI Qingquan^{1, 3, 4}

1.
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, MNR Key Laboratory for Geo-Environmental Monitoring of Coastal Zone, Shenzhen University, Shenzhen 518060, China
2.
Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China
3.
Guangdong Key Laboratory for Urban Informatics, Department of Urban Informatics, Shenzhen University, Shenzhen 518060, China
4.
Guangdong Laboratory of Artificial Intelligence and Digital Economy, Shenzhen 518060, China
5.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Funds:

The National Key Reasearch and Developmeny Program of China 2018YFB2100704

the National Natural Science Foundation of China 41701444

the National Natural Science Foundation of China 41971341

More Information

Author Bio:
LI Fan, master, specializes in spatial-temporal data mining and analysis.2170276016@email.szu.edu.cn
Corresponding author:
XIA Jizhe, PhD, assistant professor.E-mail:xiajizhe@szu.edu.cn
Received Date: September 29, 2020
Published Date: December 04, 2020

Graphical Abstract

Abstract

Abstract

Predicting the future activity location and trajectory of residents can provide essential information for smart urban management such as epidemic control, traffic facilitation, public security, etc. However, the current personal location prediction methods generally focus on the mining of individual's historical travel patterns, and seldom consider the feature of different travel stay points. This paper aims to propose a location prediction model utilizing stay point feature extraction. The model firstly constructs the historical trajectory links based on trajectory data, performs the location discovery rules to transform historical trajectory links into stay point links and clusters the stay points to form clustering links. Secondly, the model extracts the feature information (entry time, departure time, weather and land use) from different stay points and extracts the space feature from clustering links. Finally, the cluster links with feature information is introduced into long short-term memory (LSTM) network for customization to implement the personal location prediction capability. By using a 23-day trajectory location data of more than 3 million volunteer users in Shenzhen city, China. The results show that the location prediction F-score of our proposed model is better than variable order Markov model (about 5.5% performance gains) and the traditional N-order Markov model (about 7% performance gains). The model also introduces approximately 6.6% performance gains by utilizing the temporal and weather features of stay points. The model shows the capability of utilizing travel stay point feature for personal next location prediction.
- location prediction,
- spatiotemporal trajectory,
- feature extraction,
- neural network,
- long short-term memory

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References (31)

References

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