Abstract
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.
References
Calabrese, F., Ferrari, L., Blondel, V.D.: Urban sensing using mobile phone network data: a survey of research. ACM Comput. Surv. 47(2), 1–20 (2014)
Li, P., Gao, Y.W., Wu, J.W., Li, X., Wu, B.B.: Residents traveling track and analysis methods based on mobile phone data. Adv. Mater. Res. 926, 2730–2734 (2014)
Wu, M., Dong, H., Ding, X., Shan, Q., Chu, L., Jia, L.: Traffic semantic analysis based on mobile phone base station data. In: International Conference on Intelligent Transportation Systems, pp. 617–622. IEEE (2014)
Dong, H., Wu, M., Ding, X., Chu, L., Jia, L., Qin, Y., Zhou, X.: Traffic zone division based on big data from mobile phone base stations. Transp. Res. Part C: Emerg. Technol. 58, 278–291 (2015)
Ma, X., Wu, Y.J., Wang, Y., Chen, F., Liu, J.: Mining smart card data for transit riders travel patterns. Transp. Res. Part C Emerg. Technol. 36, 1–12 (2013)
Le, M.K., Bhaskar, A., Chung, E.: Passenger segmentation using smart card data. IEEE Trans. Intell. Transp. Syst. 16(3), 1537–1548 (2015)
Le, M.K., Bhaskar, A., Chung, E.: A modified density-based scanning algorithm with noise for spatial travel pattern analysis from Smart Card AFC data. Transp. Res. Part C: Emerg. Technol. 6 (2015, in press). Corrected Proof, April 2015
Fraley, C., Raftery, A.E.: Model-based clustering, discriminant analysis, and density estimation. J. Am. Stat. Assoc. 97(458), 611–631 (2002)
Cui, Z., Long, Y., Ke, R., Wang, Y.: Characterizing evolution of extreme public transit behavior using smart card data. In: 2015 IEEE 1st International Smart Cities Conference (ISC2), pp. 1–6 (2015)
Briand, A.S., Côme, E., Mohamed, K., Oukhellou, L.: A mixture model clustering approach for temporal passenger pattern characterization in public transport. Int. J. Data Sci. Anal. 1(1), 37–50 (2016)
Lee, M., Sohn, K.: Inferring the route-use patterns of metro passengers based only on travel-time data within a Bayesian framework using a reversible-jump Markov chain Monte Carlo (MCMC) simulation. Transp. Res. Part B: Methodol. 81, 1–17 (2015)
Qiao, S.J., Jin, K., Han, N., Tang, C.J., Gesangduoji, G.L.A.: Trajectory prediction algorithm based on Gaussian mixture model. J. Softw. 26(5), 1048–1063 (2015). Jian, R., BaoX. (eds.)
Wu, J., Zheng, Y., Chen, X.: Approaches to planning of subway station transfer facility in urban areas. J. Tongji Univ. (Nat. Sci.) 36(11), 1501–1506 (2008)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press (1996)
Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 160–172. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37456-2_14
Sander, J., Ester, M., Kriegel, H.P., Xu, X.: Density-based clustering in spatial databases: the algorithm gdbscan and its applications. Data Min. Knowl. Disc. 2(2), 169–194 (1998)
Yu, G., Sapiro, G., Mallat, S.: Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity. IEEE Trans. Image Process. 21(5), 2481–2499 (2012)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Ser. B (Methodol.) 1–38 (1977)
Kehtarnavaz, N., Nakamura, E.: Generalization of the EM algorithm for mixture density estimation. Pattern Recogn. Lett. 19(2), 133–140 (1998)
Acknowledgments
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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Shen, W., Wei, Z., Zhou, Z. (2017). Mining Mobile Phone Base Station Data Based on Clustering Algorithms with Application to Public Traffic Route Design. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_13
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