Abstract
Urban trace mining and modeling of large scale active mobile phone holders is of great significance as it can effectively reduce traffic jam and hit-and-run incidents as well as other urban problems. Traditionally, people monitors traffic flows by GPS devices installed on buses or cars and traces hit-and-run incidents by road cameras, which turns out to be inefficient in that the data are sparse to represent a massive of people traces. In this paper, we propose a novel approach called mobile sequence to describe urban roads and moving object trajectories. Firstly, we use real urban roads information and base stations location information to generate urban roads mobile sequence. Then we extract people’s mobile sequence from massive mobile network logs. Finally, we match roads’ mobile sequence with people’s mobile sequence to obtain human traces. As a validation, we use application installed on mobile phone to gather some real urban roads and base stations’ information and then calculate their real mobile sequences to match the theoretical mobile sequences and gain 94.8% covering rate. Also, we use people’s mobile sequences extracted from mobile network logs with theoretical mobile sequences and gain 90.6% matching accuracy.
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Acknowledgments
This work is supported by Ministry of Education-China Mobile Research Fund under grant MCM20150507 and Tsinghua University Initiative Scientific Research Program (No. 20131089190). Beijing Key Lab of Networked Multimedia also supports our research work.
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Ma, Y., Xu, B., Li, Q. (2017). Urban Trace Utilizing Mobile Sequence. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_20
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DOI: https://doi.org/10.1007/978-981-10-6388-6_20
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