Transcript ppt
Mapcube to Understand
Traffic Patterns
Shashi Shekhar
Computer Science Department
University of Minnesota
[email protected]
(612) 624-8307
http://www.cs.umn.edu/~shekhar
http://www.cs.umn.edu/research/shashi-group/
Motivation for Traffic Visualization
Transportation Manager
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
How the freeway system performed yesterday?
Which locations are worst performers?
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?
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)
Map of Station in Mpls
Dimensions
Available
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TTD : Time of Day
TDW : Day of Week
TMY : Month of Year
S : Station, Highway, All Stations
Others
• Scale, Weather, Seasons, Event types, …
Mapcube :
Which Subset of Dimensions ?
TTDTDWTMYS
TTDTDWS
TTDTDW
TDW
S
STTD
TTD
TDW
S
Next Project
Singleton Subset : TTD
Configuration: X-axis: time of day; Y-axis: Volume
For station sid 138, sid 139, sid 140, on 1/12/1997
Trends:
Station sid 139: rush hour all day long
Station sid 139 is an S-outlier
Singleton Subset: TDW
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
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)
Dimension Pair: TTD-TDW
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
Evening rush hour broader than morning rush hour
Rush hour starts early on Friday.
Wednesday - narrower evening rush hour
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
Dimension Pair: TDW-S
Configuration:
Trends:
X-axis: stations; Y-axis: day of week
f(x,y): Avg. volume over all stations for Jan-Mar 1997
Busiest segment of I-35 SW is b/w Downtown MPLS & I-62
Saturday has more traffic than Sunday
Outliers – Route branch
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
Size 4 Subset: TTDTDWTMYS(Album)
Configuration:
Trends:
Outer: X-axis (month of year); Y-axis (route)
Inner: X-axis (time of day); Y-axis (day of week)
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
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)
Data Fusion levels and Mapcube
Different Sub-cubes help with different data fusion levels
Level 0: Single Sensor
Level 1: Correlating Multiple Sensors
Local weather as a function of time
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
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