![]() Keim, D.A., Kriegel, H.-P.: Using visualization to support data mining of large existing databases. Harinarayan, V., Rajaramna, A., Ullman, J.D.: Implementing data cubes efficiently. Han, J.W., Kamber, J.: Data mining: concept and techniques, 2nd edn, pp. (eds.) Geographic Data Mining and Knowledge Discovery, 2nd edition edn, pp. Guo, D.: Multivariate spatial clustering and visualization. Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: a relational aggregation operator generalizing group-by, cross-tab and sub-totals. (eds.) Geographic Data Mining and Knowledge Discovery, 2nd edn, pp. Gahegan, M.: Visual exploration and explanation in geography: analysis with light. Gahegan, M.: The case for inductive and visual techniques in the analysis of spatial data. Department of Transportation, Publication Number: FHWA-HRT-06-139, October 2006. Morgan Kaufmann, San Matel, CA (2001)įHWA: Traffic detector handbook: third edition, federal highway administration. AAAI Press, Menlo Park, CA (1996)įayyad, U., Grinstein, G., Wierse, A.: Information Visualization in Data Mining and Knowledge Discovery. (eds.) Advances in Knowledge Discovery and Data Mining, pp. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. ![]() Elsevier Science, Oxford (1997)įayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: an overview. 9, 145–170 (2009)ĭaganzo, C.F.: Fundamentals of Transportation and Traffic Operations. Addison-Wesley, Harlow, UK (1996)Ĭho, H.-J., Jou, Y.-J., Lan, C.-L.: Time dependent origin-destination estimation from traffic count without prior information. Example visualizations of a large database of hourly traffic flows along major highways in the state of Utah (USA) over a 10-year period illustrate the potential for the toolkit to reveal patterns about traffic flows and trends hidden in the database.Īdriaans, P., Zantinge, D.: Data Mining. We demonstrate a prototype system using MATLAB, ArcGIS and MS Access database software. The toolkit allows the user to perform data cube operations to select, summarize and cross-tabulate the traffic data prior to visualization as two-dimensional space-time plots. The traffic cube organizes traffic flow data across different spatial and temporal dimensions and with respect to user-specified aggregation levels. The visualization toolkit is based on the concept of the traffic cube: an extension of the data cube in data mining. In this paper, we describe an exploratory visualization toolkit for large traffic flow databases. However, these data are rarely exploited to their full potential. Local departments of transportation and metropolitan planning organizations have been collecting traffic data for many decades.
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