Abstract
In order to alleviate the traffic congestion and reduce the complexity of traffic control and management, it is necessary to exploit traffic sub-areas division which should be effective in planing traffic. Some researchers applied the K-Means algorithm to divide traffic sub-areas on the taxi trajectories. However, the traditional K-Means algorithms faced difficulties in processing large-scale Global Position System(GPS) trajectories of taxicabs with the restrictions of memory, I/O, computing performance. This paper proposes a Parallel Traffic Sub-Areas Division(PTSD) method which consists of two stages, on the basis of the Parallel K-Means(PKM) algorithm. During the first stage, we develop a process to cluster traffic sub-areas based on the PKM algorithm. Then, the second stage, we identify boundary of traffic sub-areas on the base of cluster result. According to this method, we divide traffic sub-areas of Beijing on the real-word (GPS) trajectories of taxicabs. The experiment and discussion show that the method is effective in dividing traffic sub-areas.
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References
Palma, D.A., Lindsey, R.: Traffic congestion pricing methodologies and technologies. Transportation Research Part C: Emerging Technologies 19(6), 1377–1399 (2011)
Wåhlberg, A.E., Dorn, L., Kline, T.: The effect of social desirability on self reported and recorded road traffic accidents. Transportation Research Part F: Traffic Psychology and Behaviour 13(2), 106–114 (2010)
Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques. Morgan Kaufmann (2006)
Lv, Y.Q., Qin, Y., Jia, L.M., et al.: Dynamic Traffic Zone Partition Based on Cluster Analysis of Taxi GPS Data. Logistics Technology 29(9), 86–88 (2010)
Pham, D.T., Dimov, S.S., Nguyen, C.D.: A two-phase k-means algorithm for large datasets. Journal of Mechanical Engineering Science 218(10), 1269–1273 (2004)
Kantabutra, S., Couch, A.L.: Parallel K-means clustering algorithm on NOWs. NECTEC Technical Journal 1(6), 243–247 (2000)
Kraj, P., Sharma, A., Garge, N., et al.: ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use. BMC Bioinformatics 9(1), 200 (2008)
Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The Hadoop Distributed File System. In: 26th IEEE Symposium on Mass Storage Systems and Technologies, pp. 1–10. Incline Village (2010)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)
Nguyen, C.D., Nguyen, D.T., Pham, V.-H.: Parallel two-phase K-means. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part V. LNCS, vol. 7975, pp. 224–231. Springer, Heidelberg (2013)
Zhou, P., Lei, J., Ye, W.: Large-Scale Data Sets Clustering Based on MapReduce and Hadoop. Journal of Computational Information Systems 7(16), 5956–5963 (2011)
Xu, X., Jager, J., Kriegel, H.P.: A Fast Parallel Clustering Algorithm for Large Spatial Databases. Data Mining and Knowledge Discovery 3, 263–290 (1999)
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Wang, B. et al. (2014). Dividing Traffic Sub-areas Based on a Parallel K-Means Algorithm. In: Buchmann, R., Kifor, C.V., Yu, J. (eds) Knowledge Science, Engineering and Management. KSEM 2014. Lecture Notes in Computer Science(), vol 8793. Springer, Cham. https://doi.org/10.1007/978-3-319-12096-6_12
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DOI: https://doi.org/10.1007/978-3-319-12096-6_12
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