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GPS for Interactive Traffic Analysis af Kristian Torp, AAU
1. GPS FOR INTERACTIVE TRAFFIC
ANALYSIS
Kristian Torp
Department of Computer Science
Aalborg University, Denmark
torp@cs.aau.dk
2. Outline
• Technical part
Data foundation
Software architecture
Data-warehouse design
• Traffic-analysis part
Congestion and speed maps
Eco routing (fastest versus most fuel-efficient route)
Path analysis (routes taken between points A and B)
Trip analysis (where from and where to)
• Questions and answers
InfinIT 2014-01-15
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4. GPS and CANBus Data
id
1
2
3
4
5
id
1
2
3
4
5
Veh id
42
42
42
42
42
Lat
57.0
57.0
57.0
57.0
57.0
Long
9.9
9.9
9.9
9.9
9.9
Time
12:00:00
12:00:01
12:00:02
12:00:03
12:00:04
Velocity
80
80
70
75
80
Veh. id Fuel Breaks Temp. RPM
GPS data
42
123
0
90
1400 …
42
120
0
91
1424 …
42
90
40
90
850 …
42
12
80
90
850 …
42
239
9
90
2299 …
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5. Statistics
• ~2.5 billion GPS observations
+80 % of road network
~1.8 million GPS observations daily
• ~250 million CANBus/OBD/FMS observations
12 % of road network
~0.5 million CANBus observations daily
• +14.000 vehicles in total
~3.000 vehicles daily (roughly half in weekends)
• Data warehouse updated nightly
Used for payment of taxi drivers
• Not really “big data”
Compensate by small hardware budget
Big enough!
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17. Køge Bugt: Where to 7:00 – 9:00?
• 893 trips
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18. Køge Bugt: Where from 7:00 – 9:00?
• 893 trips
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19. Summary
• Traffic is a great application for spatio-temporal data
Daisy’s “core business” for many years
• Many applications of GPS data
Congestion and speed maps
Route-choice/path analysis
Eco-routing
Intersection analysis using trips
• Fuel consumption from CANBus data
Interest for fleet owners
Green mobility
• 1NF is not dead
1NF+SQL
SQL is a pretty good query language
Parallel data processing a necessity
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