GPS FOR INTERACTIVE TRAFFIC

ANALYSIS

Kristian Torp

Department of Computer Science

Aalborg University, Denmark

torp@cs.aau.dk

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

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Overall Idea

ITS

Software

Digital

Map

GPS Data

POI

Travel-time

Map

Congestion Map

Input Output

Path Analysis

Eco Routing CANBus

Data

…

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Trip Analysis

…

GPS and CANBus Data

id Veh id Lat Long Time Velocity

1 42 57.0 9.9 12:00:00 80

2 42 57.0 9.9 12:00:01 80

3 42 57.0 9.9 12:00:02 70

4 42 57.0 9.9 12:00:03 75

5 42 57.0 9.9 12:00:04 80

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id Veh. id Fuel Breaks Temp. RPM GPS data

1 42 123 0 90 1400 …

2 42 120 0 91 1424 …

3 42 90 40 90 850 …

4 42 12 80 90 850 …

5 42 239 9 90 2299 …

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

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• Not really “big data”

Compensate by small hardware budget

Big enough!

Software Architecture

64 bit Operating System

PostgreSQL 9.3/ Postgis 2.1

M-GEMMA Psycopg

Project-generic Code

Python 3.3

pygrametl

Project-specific Code

NetworkX

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Data Warehouse Design

Positioning

Data

Vehicle Data

Source

Batch

Load

Date Time Attributes

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Overall ETL Data Flow

GPS Data insert trip

Phase 1 Phase 2 Phase 3

point point-fact

trip-fact Map-match

data-fact

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Speed and Congestion Maps

• Maps covers all of Denmark

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Congestion

Køer i Jylland?

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Køer i Jylland?

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Randers Vej, Aarhus Limfjordstunnel

Eco-routing: Airport - AAU CPH

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Fuel 13:57 7.44 km. 0.74 l.

Fastest 10:51 9.11 km. 0.83 l.

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Eco-routing: Airport – Railway Station

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Best 17:08 8.06 km. 0.80 l.

Worse 18:06 13.64 km. 1.23 l.

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Køge Bugt – Ring 3: 15:00 – 18:00

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12 trips

761 secs

18.4 km

125 trips

655 secs

16.7 km

122 trips

669 secs

17.3 km

• 3 different routes

Køge Bugt – Ring 3: 7:00 – 9:00

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96 trips

815 secs

16.7 km

18 trips

709 secs

17.3 km

• 11 different routes

JUG?

Helsingørmotorvejen 7:00 – 9:00?

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• 855 trips

37 %

37 %

20 %

Køge Bugt: Where to 7:00 – 9:00?

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• 893 trips

Køge Bugt: Where from 7:00 – 9:00?

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• 893 trips

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

SQL is a pretty good query language

Parallel data processing a necessity

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1NF+SQL

Additional Information

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• http://daisy.aau.dk/its

• http://daisy.aau.dk/its/spqdemo

• Benjamin out-front doing demos

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