Abstract
With the popularity of bike-sharing system in cities, a large volume of bike-sharing data, which contain the spatio-temporal characteristics of riders, are generated. These data enable a number of important location-aware services such as detecting hot-spots and predicting traffic in a city. This paper develops a system for Mining and Analysing Hot-Spots (MAHS) from bike-sharing data which are collected from Beijing within a week. Firstly, according to different time granularity and space distance, MAHS visually figures the spatio-temporal statistical information of bike-sharing data by graph. Secondly, utilizing POI data from Amap API and the density peak clustering algorithm, MAHS discoveries hot-spots on start-locations and end-locations during the morning and evening rush hours, and intuitively illustrates them on map. Thirdly, based on the LDA algorithm, MAHS identifies the city functions of hot-spots and the moving patterns of riders, and concisely demonstrates them on map. MAHS can help users with managing bike-sharing system, understanding the moving patterns of riders, finding the functional regions of cities, and other urban computing.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (61662086, 61762090, 61966036), and the Project of Innovation Research Team of Yunnan Province (2018HC019).
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Wang, Y., Ma, D., Chen, H., Zhou, L. (2021). MAHS: A System for Mining and Analysing Hot-Spots from Bike-Sharing Data. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_6
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DOI: https://doi.org/10.1007/978-3-030-70665-4_6
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