Trajectory Data Warehousing

A Study on Trajectory Data Aggregation
Simone Campora
The Rio de Janeiro’s
Trajectory Data Warehouse
February 4th 2009

The Purpose:
Building a data warehousefor Rio de Janeiro TrafficDpt
Prototype a trajectorydatawarehouse
Explore DW potentialities
Challenges:
Howto integrate GPS data into a TrDW?
Whichkindof information couldbeextracted?
Discover the issues
How the resultscouldbepresented?
Introduction

Some Facts:
Numberofcarsismostlydoubledduring the last year!
Rio The Janeiro: a Metropolis

Which problems will be considered?
Congestions: is a condition on any network as use increases and is characterized by slower speeds, longer trip times, and increased queuing
Emissions of CO2: How much CO2 is produced by vehicles? Calculated on average production index of 200 gr/Km
Problems

TrafficProblem: Congestions

How to use that information?
We can extract the same information while looking at vehicles’ average speed
From Velocity to Traffic Density

Wewouldliketorunquerieslike
How the trafficcongestions are evolvingduring the week? (Spatial)
Q2: Which are the mostpollutedstreets? (Spatio-Temporal)
Q3: Whichstreets are the mostcongested? (Numeric)
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2
3
Why a TrDW for Rio?

Trajectoryis the unitof work forourtraffic management application
wepartiallyuse the trajectorymodeldeveloped (i.e. Stops-Moves)
Stopshavebeenalreadycalculated and are representedbyanattributeforeachtrajectory
Trajectorysegmentationisconstraintedby road network segmentation
Note
HowcouldTrajectoriesbehelpful?

GPS Signals
Position
Time
Speed
Street Network
Street segmentation
Street names
Trajectories
OurDataset

Time
Streets
Trajectories
Fact table entry:
A trajectory instance segment
The Star Schema

How to access MOLAP using SQL?
Oracle OLAP SQL interface

Whichis the correlationbetweenpollutioncausedby high speed and congestions?
First Query: Spatial

HowCongestions are evolvingduring week?
SecondQuery: Spatio-Temporal

ThirdQuery: Numeric
Whichstreetshaveglobally the worsttrafficconditions?

Positives:
GoodExpressivePowerforAggregations
Multi-dimensionalrepresentation
SQL interface from MOLAP toRelational
Drawbacks
ToomanyCatalogtables!
No robust bulk loadingmethods: FatalErrors!
Slow queriesalsowithsimplemappingtoRelational
Toquery a Cubewithstreets and Timedimension, itisrequired 3-4 Mins.
Limitationsofsupportedtypes:
Only TEXT, Number, Date
No ComplexObjects
Remarksusing Oracle OLAP

Conclusions
The design processisdangerous!
LackofErrorHandling
SQL interface leadstowideruses e.g. GIS tools
Future work: use OLAP DML toenhancerunningtimes

Thanksfor the Attention

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Trajectory Data Warehousing

by Simone Campora on Oct 15, 2009

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