High Performance Spatial Visualization of Traffic Data
University of Minnesota, Center for Transportation Studies, 2004 - Computer algorithms - 74 pages
Current visualizations techniques for identifying performance bottlenecks with loop-detector traffic data are not sufficient for large data sets to create interactive visualization and analysis of possible scenarios. This study seeks to develop a more effective means of processing data obtained at the Traffic Management Center (TMC) to identify recurring patterns in the traffic data that may be being lost in current data collection process. The final objective is to create a software prototype for analysis.
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Identiﬁcation of Bottleneck
3 other sections not shown
algebraic aggregate functions algorithm Applets attribute function attribute value average trafﬁc average volume classiﬁcation clustering computed Cross Tab cube operations data cube visualization data mining data set data warehouse data warehousing deﬁned deﬁnition Detector dimension hierarchy display distributive aggregate functions downtown Minneapolis drill-down dt_cube example ﬁelds Figure ﬁmction ﬁnd ﬁrst ﬁve_min Freeway group-by highway dimension I-35W north bound I-35W South identiﬁcation interface Java Jiawei Han located measures Model Building module Month multidimensional data model MySQL neighbor neighborhood aggregate function nodes OLAP pattems performance bottlenecks pivot primary key ramp meter roll-up rush hour S-dimension S-outliers scan scatterplot sensor shows slice smallint snapshot space dimension spatial data spatial outlier detection speciﬁc star schema stations on I-35W Summary trafﬁc map text ﬁle trafﬁc data trafﬁc ﬂow trafﬁc video trafﬁc volume Twin Cities users visualization of trafﬁc volume and occupancy volume map weekday whole day X-axis Y-axis