Oplægget blev holdt ved InfinIT-arrangementet Big Data og data-intensive systemer i Danmark, der blev af holdt en 15. januar 2014. Læs mere om arrangementet her: http://infinit.dk/dk/arrangementer/tidligere_arrangementer/big_data_i_danmark.htm

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



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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
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3. Overall Idea
Travel-time
Map
Digital
Map
Congestion Map
GPS Data
CANBus
Data
ITS
Software
Eco Routing
Path Analysis
POI
Trip Analysis
…
Input
…
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Output
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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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6. Software Architecture
Project-specific Code
pygrametl
Project-generic
Code
Psycopg
PostgreSQL 9.3/
Postgis 2.1
NetworkX
M-GEMMA
Python 3.3
64 bit Operating System
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7. Data Warehouse Design
Vehicle
Data
Source
Batch
Load
Positioning
Data
Date
Time
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Attributes
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8. Overall ETL Data Flow
trip
GPS Data
Map-match
insert
trip-fact
data-fact
point
Phase 1
Phase 2
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point-fact
Phase 3
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9. Speed and Congestion Maps
Congestion
• Maps covers all of Denmark
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10. Køer i Jylland?
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11. Køer i Jylland?
Randers Vej, Aarhus Limfjordstunnel
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12. Eco-routing: Airport - AAU CPH
Fuel
Fastest
13:57
10:51
7.44 km.
9.11 km.
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0.74 l.
0.83 l.
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13. Eco-routing: Airport – Railway Station
Best
Worse
17:08
18:06
8.06 km. 0.80 l.
13.64 km. 1.23 l.
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14. Køge Bugt – Ring 3: 15:00 – 18:00
12 trips
761 secs
18.4 km
122 trips
669 secs
17.3 km
125 trips
655 secs
16.7 km
• 3 different routes
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15. Køge Bugt – Ring 3: 7:00 – 9:00
JUG?
18 trips
709 secs
17.3 km
96 trips
815 secs
16.7 km
• 11 different routes
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16. Helsingørmotorvejen 7:00 – 9:00?
37 %
20 %
37 %
• 855 trips
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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
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Congestion and speed maps
Route-choice/path analysis
Eco-routing
Intersection analysis using trips
• Fuel consumption from CANBus data

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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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20. Additional Information
• http://daisy.aau.dk/its
• http://daisy.aau.dk/its/spqdemo
• Benjamin out-front doing demos
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