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

[1]	Cécile B, Lathia N, Picot-Clemente R, et al. Location Recommendation with Social Media Data[M]// Social Information Access. Cham: Springer, 2018.
[2]	Xia Jizhe, Curtin K M, Huang Jiajun, et al. A Carpool Matching Model with Both Social and Route Networks[J]. Computers, Environment and Urban Systems, 2019, 75(5): 90-102
[3]	Lian Defu, Zhu Yin, Xie Xing, et al. Analyzing Location Predictability on Location-Based Social Networks[C].The Pacific-Asia Conference in Knowledge Discovery and Data Mining, Singapore, 2014
[4]	Song Chaoming, Qu Zuhui, Blumm N, et al. Limits of Predictability in Human Mobility[J]. Science, 2010, 327(5 968): 1 018-1 021
[5]	Xia Jizhe, Yang Chaowei, Li Qingquan. Using Spatiotemporal Patterns to Optimize Earth Observation Big Data Access: Novel Approaches of Indexing, Service Modeling and Cloud Computing[J]. Computers, Environment, and Urban Systems, 2018, 72(5): 191-203
[6]	Zheng Xin, Han Jialong, Sun Aixin. A Survey of Location Prediction on Twitter[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(9): 1 652-1 671 doi: 10.1109/TKDE.2018.2807840
[7]	Zhang Junbo, Zheng Yu, Qi Dekang, et al.Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks [J]. Artificial Intelligence, 2017, 259(9): 182-194
[8]	Jia Tao, Yan Penggao. Predicting Citywide Road Traffic Flow Using Deep Spatiotemporal Neural Networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 12(12): 1-11
[9]	詹平, 郭菁, 郭薇.基于时空索引结构的移动对象将来时刻位置预测[J].武汉大学学报(工学版), 2007, 40(3): 103-108 Zhan Ping, Guo Jing, Guo Wei. Query Processing About Near Future Positions of Moving Objects Based on Spatio-Temporal Index Structure[J]. Engineering Journal of Wuhan University, 2007, 40(3): 103-108
[10]	柯宏发, 何可, 陈永光.运动目标的MGM(1, N)轨迹预测算法[J].武汉大学学报·信息科学版, 2012, 37(6): 35-39 http://ch.whu.edu.cn/article/id/229 Ke Hongfa, He Ke, Chen Yongguang. Trajectory Prediction Algorithm of Moving Object Based on MGM(1, N)[J].Geomatics and Information Science of Wuhan University, 2012, 37(6): 35-39 http://ch.whu.edu.cn/article/id/229
[11]	邓敏, 陈倜, 杨文涛.融合空间尺度特征的时空序列预测建模方法[J].武汉大学学报·信息科学版, 2015, 40(12): 1 625-1 632 doi: 10.13203/j.whugis20130842 Deng Min, Chen Ti, Yang Wentao. A New Method of Modeling Spatio-temporal Sequence by Considering Spatial Scale Characteristics[J]. Geomatics and Information Science of Wuhan University, 2015, 40(12): 1 625-1 632 doi: 10.13203/j.whugis20130842
[12]	Keles I, Ozer M, Toroslu I H, et al. Location Prediction of Mobile Phone Users Using Apriori-Based Sequence Mining with Multiple Support[C]. International Workshop on New Frontiers in Mining Complex Patterns, Wurzbury, Germany, 2015
[13]	Chen Pengfei, Shi Wenzhong, Zhou Xiaolin, et al. STLP-GSM: A Method to Predict Future Locations of Individuals Based on Geotagged Social Media Data[J]. International Journal of Geographical Information Systems, 2019, 33(12): 2 337-2 362 doi: 10.1080/13658816.2019.1630630
[14]	段炼, 胡涛, 朱欣焰, 等.顾及时空语义的疑犯位置时空预测[J].武汉大学学报·信息科学版, 2019, 44(5): 765-770 doi: 10.13203/j.whugis20170238 Duan Lian, Hu Tao, Zhu Xinyan, et al. Spatio-Temporal Prediction of Suspect Location by Spatio-Temporal Semantics[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 765-770 doi: 10.13203/j.whugis20170238
[15]	Du Yongping, Wang Chencheng, Qiao Yanlei, et al. A Geographical Location Prediction Method Based on Continuous Time Series Markov Model[J]. PloS One, 2018, 13(11): 152-171 http://www.ncbi.nlm.nih.gov/pubmed/30452446
[16]	Li Fan, Li Qingquan, Li Zhen, et al. A Personal Location Prediction Method Based on Individual Trajectory and Group Trajectory[J]. IEEE Access, 2019, 7(7): 92 850-92 860
[17]	Li Fan, Li Qingquan, Li Zhen, et al. A Personal Location Prediction Method to Solve the Problem of Sparse Trajectory Data[C]. The 20th IEEE International Conference on Mobile Data Management (MDM), Hong Kong, China, 2019
[18]	Alahi A, Goel K, Ramanathan V, et al. Social LSTM: Human Trajectory Prediction in Crowded Spaces[C]. The 2016 IEEE Conference on Computer Vision and Pattern Recognition, New York, USA, 2016
[19]	Wu Fan, Fu Kun, Wang Yang, et al. A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects[J]. Algorithms, 2017, 10(2): 37-40 doi: 10.3390/a10020037
[20]	Wong M H, Tseng V S, Tseng J C C, et al. Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining[C]. International Conference on Advanced Data Mining and Applications, Singapore, 2017
[21]	李明晓, 张恒才, 仇培元, 等.一种基于模糊长短期神经网络的移动对象轨迹预测算法[J].测绘学报, 2018, 47(12): 102-111 Li Mingxiao, Zhang Hengcai, Qiu Peiyuan, et al. Predicting Future Locations with Deep Fuzzy-LSTM Network[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(12): 102-111
[22]	Ying J J, Lee W, Tseng V S. Mining Geographic-Temporal-Semantic Patterns in Trajectories for Location Prediction[J]. ACM Transactions on Intelligent Systems and Technology, 2013, 5(1): 1-33
[23]	王津铭.基于变阶Markov和LSTM的位置预测技术研究[D].北京: 北京邮电大学, 2018 Wang Jinming. Research on Semantic Location Prediction Technology Using Variable Order Markov and LSTM[D]. Beijing: Beijing University of Posts and Telecommunications, 2018
[24]	Jain A, Zamir A R, Savarese S, et al. Structural-RNN: Deep Learning on Spatio-Temporal Graphs[C]. Computer Vision and Pattern Recognition, New York, USA, 2016
[25]	Greff K, Srivastava R K, Koutník J, et al. LSTM: A Search Space Odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 28(10): 2 222-2 232
[26]	Xu K, Ba J, Kiros R, et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention[C]. The International Conference on Machine Learning, Toronto, USA, 2015
[27]	Zheng Yu, Zhang Lizhu, Xie Xing, et al. Mining Interesting Locations and Travel Sequences from GPS Trajectories[C]. The International Conference on World Wide Web, Madrid, Spain, 2009
[28]	Li Quannan, Zheng Yu, Xie Xing, et al.Mining User Similarity Based on Location History[C]. The International Conference on Advances in Geographic Information Systems, New York, USA, 2008
[29]	Yue Yang, Zheng Yu, Chen Yukun, et al. Mining Individual Life Pattern Based on Location History[C]. The International Conference on Mobile Data Management, Taipei, China, 2009
[30]	Ester M, Kriegel H, Sander J, et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C].The International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, USA, 1996
[31]	Macqueen J B. Some Methods for Classification and Analysis of Multivariate Observations[C]. Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, 1965

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