A New Approach to Measuring the Similarity of Indoor Semantic Trajectories
People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be d...
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/2/90 |
| _version_ | 1797395885607354368 |
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| author | Jin Zhu Dayu Cheng Weiwei Zhang Ci Song Jie Chen Tao Pei |
| author_facet | Jin Zhu Dayu Cheng Weiwei Zhang Ci Song Jie Chen Tao Pei |
| author_sort | Jin Zhu |
| collection | DOAJ |
| description | People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers. |
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| id | doaj.art-00c93f61a35f4de394338eaff7f1edbe |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T00:42:17Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
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| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-00c93f61a35f4de394338eaff7f1edbe2023-12-11T17:44:42ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-02-011029010.3390/ijgi10020090A New Approach to Measuring the Similarity of Indoor Semantic TrajectoriesJin Zhu0Dayu Cheng1Weiwei Zhang2Ci Song3Jie Chen4Tao Pei5State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaSchool of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaPeople spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.https://www.mdpi.com/2220-9964/10/2/90indoor trajectory similaritysemantic similarityedit distanceindoor positioning dataindoor walking distance |
| spellingShingle | Jin Zhu Dayu Cheng Weiwei Zhang Ci Song Jie Chen Tao Pei A New Approach to Measuring the Similarity of Indoor Semantic Trajectories ISPRS International Journal of Geo-Information indoor trajectory similarity semantic similarity edit distance indoor positioning data indoor walking distance |
| title | A New Approach to Measuring the Similarity of Indoor Semantic Trajectories |
| title_full | A New Approach to Measuring the Similarity of Indoor Semantic Trajectories |
| title_fullStr | A New Approach to Measuring the Similarity of Indoor Semantic Trajectories |
| title_full_unstemmed | A New Approach to Measuring the Similarity of Indoor Semantic Trajectories |
| title_short | A New Approach to Measuring the Similarity of Indoor Semantic Trajectories |
| title_sort | new approach to measuring the similarity of indoor semantic trajectories |
| topic | indoor trajectory similarity semantic similarity edit distance indoor positioning data indoor walking distance |
| url | https://www.mdpi.com/2220-9964/10/2/90 |
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