Individual mobility prediction by considering current traveling features and historical activity chain
Individual mobility prediction forecasts traveling activities of an individual traveler, and has wide applications in location-based services, public health, and transportation planning. Whereas, it remains challenging due to the complexity and uncertainty of human mobility. Existing methods mainly...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2455005 |
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| author | Xiaotong Zhang Zhipeng Gui Yuhang Liu Dehua Peng Qianxi Lan Zhangxiao Shen Huan Chen Yuhui Zuo Yao Yao Huayi Wu Kai Li Kun Qin |
| author_facet | Xiaotong Zhang Zhipeng Gui Yuhang Liu Dehua Peng Qianxi Lan Zhangxiao Shen Huan Chen Yuhui Zuo Yao Yao Huayi Wu Kai Li Kun Qin |
| author_sort | Xiaotong Zhang |
| collection | DOAJ |
| description | Individual mobility prediction forecasts traveling activities of an individual traveler, and has wide applications in location-based services, public health, and transportation planning. Whereas, it remains challenging due to the complexity and uncertainty of human mobility. Existing methods mainly consider spatiotemporal contexts in current traveling, but overlook those in historical trips, as well as relationships between traversed road intersections. These issues hinder the model from effectively capturing complex mobility patterns. To fill this gap, we propose a novel method that incorporates current traveling features and historical activity chain to predict the coordinates of traveling destination. Specifically, (1) we construct current traveling features by extracting real-time moving states, and represent spatiotemporal correlations between traversed road intersections using word embedding; (2) we learn travel intentions as a probability vector for each historical trip, and combine it with spatiotemporal features to construct historical activity chain; (3) we construct an individual mobility prediction model using Long Short-Term Memory (LSTM) network and spatiotemporal scoring mechanism, to capture short-term and long-term dependencies in current trip and historical activity chain, respectively. Experiments on 21,890 trajectories over the whole Year 2019 of 20 representatives selected from 1916 private car travelers in Shenzhen City, reveal the effectiveness of our model. It outperforms four baselines, Random Forest (RF), Distant Neighboring Dependencies (DND), Location Semantics and Location Importance (LSI)-LSTM, as well as Intersection Transfer Preference and Current Movement Mode (ITP-CMM), by approximately 10%-15% improvement in accuracy. In addition, we further explore the impact of historical activity chain length, and destination visiting frequency on prediction, as well as the relationship between predictability and eight mobility pattern features. This study benefits potential applications such as personalized location-based service recommendations and targeted advertising, and also provides implications for understanding human mobility. |
| format | Article |
| id | doaj-art-133336e411ce445ea1ad76396e0f47a5 |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-133336e411ce445ea1ad76396e0f47a52025-02-04T15:26:10ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-01-0112810.1080/10095020.2025.2455005Individual mobility prediction by considering current traveling features and historical activity chainXiaotong Zhang0Zhipeng Gui1Yuhang Liu2Dehua Peng3Qianxi Lan4Zhangxiao Shen5Huan Chen6Yuhui Zuo7Yao Yao8Huayi Wu9Kai Li10Kun Qin11School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaClimate Change and Energy Economics Study Center, School of Economics and Management, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaIndividual mobility prediction forecasts traveling activities of an individual traveler, and has wide applications in location-based services, public health, and transportation planning. Whereas, it remains challenging due to the complexity and uncertainty of human mobility. Existing methods mainly consider spatiotemporal contexts in current traveling, but overlook those in historical trips, as well as relationships between traversed road intersections. These issues hinder the model from effectively capturing complex mobility patterns. To fill this gap, we propose a novel method that incorporates current traveling features and historical activity chain to predict the coordinates of traveling destination. Specifically, (1) we construct current traveling features by extracting real-time moving states, and represent spatiotemporal correlations between traversed road intersections using word embedding; (2) we learn travel intentions as a probability vector for each historical trip, and combine it with spatiotemporal features to construct historical activity chain; (3) we construct an individual mobility prediction model using Long Short-Term Memory (LSTM) network and spatiotemporal scoring mechanism, to capture short-term and long-term dependencies in current trip and historical activity chain, respectively. Experiments on 21,890 trajectories over the whole Year 2019 of 20 representatives selected from 1916 private car travelers in Shenzhen City, reveal the effectiveness of our model. It outperforms four baselines, Random Forest (RF), Distant Neighboring Dependencies (DND), Location Semantics and Location Importance (LSI)-LSTM, as well as Intersection Transfer Preference and Current Movement Mode (ITP-CMM), by approximately 10%-15% improvement in accuracy. In addition, we further explore the impact of historical activity chain length, and destination visiting frequency on prediction, as well as the relationship between predictability and eight mobility pattern features. This study benefits potential applications such as personalized location-based service recommendations and targeted advertising, and also provides implications for understanding human mobility.https://www.tandfonline.com/doi/10.1080/10095020.2025.2455005Mobility predictionword embeddingspatiotemporal mobility patternstrajectory feature modeling |
| spellingShingle | Xiaotong Zhang Zhipeng Gui Yuhang Liu Dehua Peng Qianxi Lan Zhangxiao Shen Huan Chen Yuhui Zuo Yao Yao Huayi Wu Kai Li Kun Qin Individual mobility prediction by considering current traveling features and historical activity chain Geo-spatial Information Science Mobility prediction word embedding spatiotemporal mobility patterns trajectory feature modeling |
| title | Individual mobility prediction by considering current traveling features and historical activity chain |
| title_full | Individual mobility prediction by considering current traveling features and historical activity chain |
| title_fullStr | Individual mobility prediction by considering current traveling features and historical activity chain |
| title_full_unstemmed | Individual mobility prediction by considering current traveling features and historical activity chain |
| title_short | Individual mobility prediction by considering current traveling features and historical activity chain |
| title_sort | individual mobility prediction by considering current traveling features and historical activity chain |
| topic | Mobility prediction word embedding spatiotemporal mobility patterns trajectory feature modeling |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2025.2455005 |
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