Abstract
In order to get the characteristics of travel behavior and reduce the cost of experiment, this paper presents a travel behavior clustering algorithm, sampling-based DBSCAN (SB-DBSCAN), which uses a sampling technique based on density-based spatial clustering of applications with noise (DBSCAN). By introducing sampling technique, SB-DBSCAN can obtain the approximate distribution of original data set. The improved algorithm solves the problem that DBSCAN needs large capacity memory when analyzing massive data. The effects of sampling rate and neighborhood radius on the efficiency of SB-DBSCAN are analyzed by experiments. After comparing SB-DBSCAN with DBSCAN, the result shows that SB-DBSCAN can maintain the effect of clustering, with a better scalability and less time cost simultaneously. Finally, SB-DBSCAN is used to analyze the travel behavior, which provides decision support for urban planning and construction.
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Acknowledgments
The research work is supported by National Natural Science Foundation of China (U1433116) and Foundation of Graduate Innovation Center in NUAA (kfjj20151602).
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Tang, W., Pi, D., He, Y. (2016). A Density-Based Clustering Algorithm with Sampling for Travel Behavior Analysis. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_25
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DOI: https://doi.org/10.1007/978-3-319-46257-8_25
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