Abstract
With the development of cities, analyzing people flow in city become more and more important. Meanwhile, with the development of intelligence sensing technology especially mobile crowd-sensing, the concept of smart city was proposed by many scholars, and sensing data in smart cities provides the possibility for analysis of people flow. Based on the idea of protecting users, this paper analyzing people flow from OD trip data that not including user information with a simple structure by an improved density-based clustering algorithm named ST-DBSCAN based on the thinking of clustering; then introduce some improvements of the clustering algorithm to adapt to the urban environment, Including the use of spherical distance formulas, adding iterative steps and defining cluster centers; finally experiment on a real dataset of Nanjing, China, analyze the results and interpret some insights of the results.
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Acknowledgement
This research was supported by Defense Industrial Technology Development Program under Grant No. JCKY2016605B006, Six talent peaks project in Jiangsu Province under Grant No. XYDXXJS-031.
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Sun, T., Zhao, Y., Lian, Z. (2020). People Flow Analysis Based on Anonymous OD Trip Data. In: Tian, Y., Ma, T., Khan, M. (eds) Big Data and Security. ICBDS 2019. Communications in Computer and Information Science, vol 1210. Springer, Singapore. https://doi.org/10.1007/978-981-15-7530-3_19
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DOI: https://doi.org/10.1007/978-981-15-7530-3_19
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