A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method
Most traditional trajectory compression methods, such as the Douglas–Peucker (DP) method, consider only spatial characteristics and disregard contextual factors, including environmental context. This paper proposes a new way of trajectory formulation by considering all spatial, internal, environment...
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MDPI AG
2025-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/2/58 |
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| author | Saeed Mehri Navid Hooshangi Navid Mahdizadeh Gharakhanlou |
| author_facet | Saeed Mehri Navid Hooshangi Navid Mahdizadeh Gharakhanlou |
| author_sort | Saeed Mehri |
| collection | DOAJ |
| description | Most traditional trajectory compression methods, such as the Douglas–Peucker (DP) method, consider only spatial characteristics and disregard contextual factors, including environmental context. This paper proposes a new way of trajectory formulation by considering all spatial, internal, environmental, and semantic contexts to capture all contextual aspects of moving objects. Then, we propose the Context-Aware Douglas–Peucker (CADP) method for trajectory compression. These facts are confirmed by experiments with real AIS data showing that, while CADP preserves the same computational efficiency of DP (i.e., at <i>O</i>(<i>n</i><sup>2</sup>)), it outperforms DP and two-stage Context-Aware Piecewise Linear Segmentation (two-stage CPLS) methods in preserving agent movement behavior, obtaining compressed trajectories that are closer to the original ones and that are much more useful in base analyses such as trajectory prediction. Specifically, the LSTM-based models trained on CADP-compressed trajectories have relatively lower RMSEs than others compressed by either DP or two-stage CPLS. Therefore, CADP is more scalable and efficient, thus making it more practical for large-scale engineering applications; with the improvement in trajectory analysis accuracy achieved by the suggested method, a wide range of critical engineering applications can be potentially improved, such as collision avoidance and route planning. Future work will focus on spatial auto-correlation and uncertainty to extend the robustness and applicability of the approach. |
| format | Article |
| id | doaj-art-c0b886cd9c104973bec727fccc1d18fd |
| institution | DOAJ |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-c0b886cd9c104973bec727fccc1d18fd2025-08-20T03:12:22ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-02-011425810.3390/ijgi14020058A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression MethodSaeed Mehri0Navid Hooshangi1Navid Mahdizadeh Gharakhanlou2Geomatics Engineering Department, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, IranDepartment of Geoscience Engineering, Arak University of Technology, Arak 38181-46763, IranLaboratory of Environmental Geosimulation (LEDGE), Department of Geography, University of Montreal, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, QC H2V 0B3, CanadaMost traditional trajectory compression methods, such as the Douglas–Peucker (DP) method, consider only spatial characteristics and disregard contextual factors, including environmental context. This paper proposes a new way of trajectory formulation by considering all spatial, internal, environmental, and semantic contexts to capture all contextual aspects of moving objects. Then, we propose the Context-Aware Douglas–Peucker (CADP) method for trajectory compression. These facts are confirmed by experiments with real AIS data showing that, while CADP preserves the same computational efficiency of DP (i.e., at <i>O</i>(<i>n</i><sup>2</sup>)), it outperforms DP and two-stage Context-Aware Piecewise Linear Segmentation (two-stage CPLS) methods in preserving agent movement behavior, obtaining compressed trajectories that are closer to the original ones and that are much more useful in base analyses such as trajectory prediction. Specifically, the LSTM-based models trained on CADP-compressed trajectories have relatively lower RMSEs than others compressed by either DP or two-stage CPLS. Therefore, CADP is more scalable and efficient, thus making it more practical for large-scale engineering applications; with the improvement in trajectory analysis accuracy achieved by the suggested method, a wide range of critical engineering applications can be potentially improved, such as collision avoidance and route planning. Future work will focus on spatial auto-correlation and uncertainty to extend the robustness and applicability of the approach.https://www.mdpi.com/2220-9964/14/2/58trajectorycompressioncontext awarenessdata compressionDouglas–Peuckerprediction |
| spellingShingle | Saeed Mehri Navid Hooshangi Navid Mahdizadeh Gharakhanlou A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method ISPRS International Journal of Geo-Information trajectory compression context awareness data compression Douglas–Peucker prediction |
| title | A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method |
| title_full | A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method |
| title_fullStr | A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method |
| title_full_unstemmed | A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method |
| title_short | A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method |
| title_sort | novel context aware douglas peucker cadp trajectory compression method |
| topic | trajectory compression context awareness data compression Douglas–Peucker prediction |
| url | https://www.mdpi.com/2220-9964/14/2/58 |
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