Mining Mobile Phone Base Station Data Based on Clustering Algorithms with Application to Public Traffic Route Design
It attracts a lot of attention that how to use mobile phone base station data to predict user behavior and design the public traffic route. In this paper, we extend the classic algorithms to design the shuttle bus route. The contribution of this paper is mainly manifested on (1) we integrate the classical machine learning methods DBSCAN and GMM to complete mobile phone base station data modeling, so that to learn the residents’ spatial travel pattern and temporal habits; (2) we apply the Public Route Scale Estimation Model to design the shuttle bus routes and departure intervals based on the modeling results of (1). Experimental results show that our model based on DBSCAN and GMM can effectively mine the significance of historical data of mobile phone base station and can successfully be applied to real-world problems like public traffic route design.
KeywordsPhone base station data DBSCAN GMM Travelling behavior analysis Public traffic route design
The work is partially supported by the National Nature Science Foundation of China (Nos. 61573259 and 61673301), the program of Further Accelerating the Development of Chinese Medicine Three Year Action of Shanghai (No. ZY3-CCCX-3-6002), and the National Science Foundation of Shanghai (No. 15ZR1443800).
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