Mapcube to Understand Traffic Patterns
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Transcript of Mapcube to Understand Traffic Patterns
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Mapcube to Understand Traffic Patterns
Shashi Shekhar
Computer Science DepartmentUniversity of Minnesota
[email protected](612) 624-8307
http://www.cs.umn.edu/~shekharhttp://www.cs.umn.edu/research/shashi-group/
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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?
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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)
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Map of Station in Mpls
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Dimensions
Available• TTD : Time of Day
• TDW : Day of Week
• TMY : Month of Year• S : Station, Highway, All Stations
Others• Scale, Weather, Seasons, Event types,
…
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Mapcube : Which Subset of Dimensions ?
TTDTDWS
TTDTDW TDWS STTD
TTD TDWS
TTDTDWTMYS
Next Project
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Singleton Subset : TTD
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
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Singleton Subset: TDW
Configuration: X axis: Day of week; Y axis: Avg. volume.For stations 4, 8, 577Avg. volume for Jan 1997
Trends:Friday is the busiest day of weekTuesday is the second busiest day of week
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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)
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Dimension Pair: TTD-TDW
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
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Dimension Pair: S-TTD
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 9th
• Time: 2:35pm Missing Data
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Dimension Pair: TDW-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:
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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
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Size 4 Subset: TTDTDWTMYS(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
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Triplet: TTDTDWS: 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)
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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
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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