Abstract
Traffic zone division plays an important role in analyzing traffic flow and the trend of city traffic. A traditional method based on sampling investigation has the shortcomings of high cost, long period and low sampling precision. With the development of traffic control and management methods, some cluster methods for location points are proposed to be used in the division of traffic plot. However, simple clustering analysis often need to detect the boundary of traffic zones, and the boundary of the division result is not clear, furthermore, abnormal data has great influence on results. In order to make the traffic zone division results clear and accurate and reduce the cost of the division, this paper proposes a novel grid based K-Means cluster method for traffic zone division by using Taxi GPS data. The experiment used GPS data of Nanjing city taxies and automatically divided traffic zones in Nanjing City area, which verified the validity of this partition method. The experimental results show that this classification method is effective and gives a good reference value for the analysis of city traffic flow and trends.
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References
Ratti, C., Pulseli, R.M., Williams, S., Frenchman, D.: Mobile landscapes: using location data from cell phones for urban analysis. Environ. Plann. B Plann. Des. 33(5), 727–748 (2006)
González, M.C., Hidalgo, C.A., Barabási, A.L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)
Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Pervasive and Ubiquitous Comput. 7(5), 275–286 (2003)
Farrahi, K., Gatica-Perez, D.: Learning and predicting multimodal daily life patterns from cell phones. In: Proceedings of the 11th International Conference on Multimodal Interfaces and the 6th Workshop on Machine Learning for Multimodal Interface, pp. 227–280 (2009)
Reades, J., Calabrese, F., Sevtsuk, A., Ratti, C.: Cellular census: explorations in urban data collection. IEEE Pervasive Comput. 6(3), 30–38 (2007)
Chang, H., Tai, Y., Hsu, J.Y.: Context-aware taxi demand hotspots prediction. Int. J. Bus. Intell. Data Min. 5(1), 3–18 (2010)
Phithakkitnukoon, S., Veloso, M., Bento, C., Biderman, A., Ratti, C.: Taxi-Aware map: identifying and predicting vacant taxis in the city. In: de Ruyter, B., Wichert, R., Keyson, D.V., Markopoulos, P., Streitz, N., Divitini, M., Georgantas, N., Mana Gomez, A. (eds.) AmI 2010. LNCS, vol. 6439, pp. 86–95. Springer, Heidelberg (2010)
Wong, K.I., Wong, K.C., Bell, M.G.H., Yang, H.: Modeling the bilateral micro-searching behavior for urban taxi services using the absorbing market chain approach. J. Adv. Transp. 39, 81–104 (2005)
Yang, H., Fung, C.S., Wong, K.I., Wong, S.C.: Nonlinear pricing of taxi services. Transp. Res. Part A Policy Pract. 44(5), 337–348 (2010)
Liu, L., Andris, C., Ratti, C.: Uncovering cabdrivers’ behavior patterns from their digital traces. Environment and Urban Systems, Corrected Proof, Available online 16 August (2010, in Press)
Liu, L., Andris, C., Biderman, A., Ratti, C.: Uncovering taxi driver’s mobility intelligence through his trace. IEEE Pervasive Comput. 160, 1–17 (2009)
China Institute of Highway Traffic Engineering Manual. Traffic Engineering Handbook. China Communications Press, Beijing (2001)
Li, X.D., Yang, X.G., Chen, H.J.: Study on traffic zone division based on spatial clustering analysis. Comput. Eng. Appl. 45(5), 19–22 (2009)
Yang, B., Lu, H.: Improvement of the method about the partition of traffic zone. Traffic Transp. 23(7), 23–26 (2007)
Xu, J.Q.: Traffic Engineering Subjects. China Communications Press, Beijing (2003)
Zhong, Z., Huang, W., Ma, W.: Design of traffic zone division algorithm for coordinated control with the implementation. In: The Fourth Session of the China Intelligent Transport Annual Meeting Proceedings (2008)
Ma, C.Q., Wang, R., Wang, Y.P., et al.: Calculating method of traffic zone radius in city based on inner trip proportion. J. Traffic and Transp. Eng. 7(1), 68–72 (2007)
Zhou, W., Dong, X., Xu, W., et al.: Mobile phone information based transportation cell mapping method preliminary study. In: The Fourth Session of the China Intelligent Transport Annual Meeting Proceedings (2008)
Yang, Z.Z., Wang, L., Cheng, G.: Method of traffic analysis zone partition for traffic impact evaluation. J. Highway Transp. Res. 6 24(6), 102–105,117 (2007)
Steinhaus, H.: Sur la division des corps matériels en parties. Bull. Acad. Polon. Sci. (in French) 4(12), 801–804 (1957)
Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Inf. Theor. 28(2), 129–137 (1982)
MacQueen, J.B: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press (1967)
Wilpon, J.G., Rabiner, L.R.: A modified K-Means clustering algorithm for use in isolated word recognition. IEEE Trans. Acoust. Speech Signal Process. 33(3), 587–594 (1985)
McBratney, A.B., De Gruijter, J.J.: A continuum approach to soil classification by modified fuzzy K-Means with extra grades. J. Soil Sci. 43(1), 159–175 (1992)
Simek, J.F.: A K-Means approach to the analysis of spatial structure in upper paleolithic habitation sites, pp. 400–401. B.A.R., Oxford (1984)
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Zheng, Y., Zhao, G., Liu, J. (2015). A Novel Grid Based K-Means Cluster Method for Traffic Zone Division. In: Qiang, W., Zheng, X., Hsu, CH. (eds) Cloud Computing and Big Data. CloudCom-Asia 2015. Lecture Notes in Computer Science(), vol 9106. Springer, Cham. https://doi.org/10.1007/978-3-319-28430-9_13
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DOI: https://doi.org/10.1007/978-3-319-28430-9_13
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