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Relationships of Compression Ratio and Error in Trajectory Simplification Algorithms

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Technologies and Innovation (CITI 2021)

Abstract

GPS trajectory simplification algorithms are of great importance for GPS data analysis and processing. The correct selection of these algorithms in accordance with the type of trajectory to be analyzed facilitates the reduction of storage and processing space in data analysis. This paper analyzes the correlation between the compression ratio of GPS trajectory simplification algorithms and their margin of error. These metrics measure the effectiveness of simplification algorithms in general and this work focuses specifically on batch simplification. For this purpose, coordinates in GPS trajectories of different sets of data are used and the algorithms are executed on them, taking into account that the analysis performed for the simplification process takes into account the beginning and the end of each trajectory. Finally, the data obtained from the experiments are presented in tables and figures. The experiments show that TD-TR has better performance than Douglas-Peucker algorithm due to the selected variables.

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Notes

  1. 1.

    https://github.com/gary-reyes-zambrano/Guayaquil-DataSet.git.

  2. 2.

    https://www.microsoft.com/en-us/research/publication/tdrive-trajectory-data-sample.

  3. 3.

    https://archive.ics.uci.edu/ml/machine-learning-databases/00354/.

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Reyes, G., Maquilón, V., Estrada, V. (2021). Relationships of Compression Ratio and Error in Trajectory Simplification Algorithms. In: Valencia-García, R., Bucaram-Leverone, M., Del Cioppo-Morstadt, J., Vera-Lucio, N., Jácome-Murillo, E. (eds) Technologies and Innovation. CITI 2021. Communications in Computer and Information Science, vol 1460. Springer, Cham. https://doi.org/10.1007/978-3-030-88262-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-88262-4_10

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