1. Mapcube to Understand Traffic Patterns Shashi Shekhar Computer Science Department University of Minnesota shekhar@cs.umn.edu@cs.umn.edu (612) 624-8307 http://www.cs.umn.edu/~shekhar http://www.cs.umn.edu/research/shashi-group/

2. Motivation for Traffic Visualization Transportation Manager How the freeway system performed yesterday? Which locations are worst performers? Traffic Engineering Where are the congestion (in time and space)? Which of these recurrent congestion? Which loop detection are not working properly? How congestion start and spread? Traveler, Commuter What is the travel time on a route? Will I make to destination in time for a meeting? Where are the incident and events? Planner and Research How much can information technique to reduce congestion? What is an appropriate ramp meter strategy given specific evolution of congestion phenomenon?

3. Contributions Transportation Domain Allow intuitive browsing of loop detector data Highlight patterns in data for further study Computer Science Mapcube - Organize visualization using a dimension lattice Visual data mining Hotspots, clustering (slides 9, 10 & 11) Discontinuity, Sharp Gradients, Discontinuities (slides 7 & 11) Co-locations, co-occurrences Location classification and predication (slide 13)

4. Map of Station in Mpls

5. Dimensions Available T TD : Time of Day T DW : Day of Week T MY : Month of Year S : Station, Highway, All Stations Others Scale, Weather, Seasons, Event types, …

6. Mapcube : Which Subset of Dimensions ? T TD T DW S T TD T DW T DW S ST TD T TD T DW S T TD T DW T MY S Next Project

7. Singleton Subset : T TD  X-axis: time of day; Y-axis: Volume  For station sid 138, sid 139, sid 140, on 1/12/1997 Configuration: Trends:  Station sid 139: rush hour all day long  Station sid 139 is an S-outlier

8. Singleton Subset: T DW Configuration: X axis: Day of week; Y axis: Avg. volume. For stations 4, 8, 577 Avg. volume for Jan 1997 Trends:  Friday is the busiest day of week  Tuesday is the second busiest day of week

9. Singleton Subset: S Configuration:  X-axis: I-35W South; Y-axis: Avg. traffic volume  Avg. traffic volume for January 1997 Trends?: High avg. traffic volume from Franklin Ave to Nicollet Ave Two outliers: 35W/26S(sid 576) and 35W/TH55S(sid 585)

10. Dimension Pair: T TD -T DW Evening rush hour broader than morning rush hour Rush hour starts early on Friday. Wednesday - narrower evening rush hour Configuration: Trends: X-axis: time of date; Y-axis: day of Week f(x,y): Avg. volume over all stations for Jan 1997, except Jan 1, 1997

11. Dimension Pair: S-T TD Configuration: X-axis: Time of Day Y-axis: Route f(x,y): Avg. volume over all stations for 1/15, 1997 Trends:  3-Clusters North section:Evening rush hour Downtown area: All day rush hour South section:Morning rush hour  Spatial Outliers, Discontintuities station ranked 9 th Time: 2:35pm  Missing Data

12. Dimension Pair: T DW -S Busiest segment of I-35 SW is b/w Downtown MPLS & I-62 Saturday has more traffic than Sunday Outliers – Route branch Configuration: X-axis: stations; Y-axis: day of week f(x,y): Avg. volume over all stations for Jan-Mar 1997 Trends:

13. Post Processing of cluster patterns Clustering Based Classification:  Class 1: Stations with Morning Rush Hour  Class 2: Stations Evening Rush Hour  Class 3: Stations with Morning + Evening Rush Hour

14. Size 4 Subset: T TD T DW T MY S(Album) Configuration:  Outer: X-axis (month of year); Y-axis (route)  Inner: X-axis (time of day); Y-axis (day of week) Trends:  Morning rush hour: I-94 East longer than I-35 W North  Evening rush hour: I-35W North longer than I-94 East  Evening rush hour on I-94 East: Jan longer than Feb

15. Triplet: T TD T DW S: Compare Traffic Videos Configuration: Traffic volume on Jan 9 (Th) and 10 (F), 1997 Trends:  Evening rush hour starts earlier on Friday  Congested segments: I-35W (downtown Mpls – I-62); I-94 (Mpls – St. Paul); I-494 ( intersection I-35W)

16. Data Fusion levels and Mapcube Different Sub-cubes help with different data fusion levels Level 0: Single Sensor Local weather as a function of time Level 1: Correlating Multiple Sensors Map of spatial variation in weather Space-time plot for a route for a day Level 2: Interpret, Aggregate Detect spatial discontinuities, spatial outliers Group sensors with similar weather measurements Group timeslots with similar weather measurements

17. Spatial Data Mining, SDBMS Historical Examples London Cholera (1854) Dental health in Colorado Current Examples Environmental justice Crime mapping - hot spots (NIJ) Cancer clusters (CDC) Habitat location prediction (Ecology) Site selection, assest tracking, spatial outliers