1. A Study on Trajectory
February 4th 2009
 The Purpose:
◦ Building a data warehouse for Rio de Janeiro Traffic Dpt
◦ Prototype a trajectory datawarehouse
◦ Explore DW potentialities
◦ How to integrate GPS data into a TrDW?
◦ Which kind of information could be extracted?
◦ Discover the issues
◦ How the results could be presented?
3. Rio The Janeiro: a Metropolis
HDI (2000) 0.842 – high
 Some Facts:
 Number of cars is mostly doubled during
the last year!
 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
5. Traffic Problem: Congestions
6. From Velocity to Traffic Density
 How to use that information?
◦ We can extract the same information while
looking at vehicles’ average speed
-> ∞ 140
7. Why a TrDW for Rio?
 We would like to run queries like
◦ How the traffic congestions are evolving during
the week? (Spatial)
◦ Q2: Which are the most polluted streets?
◦ Q3: Which streets are the most congested?
8. How could Trajectories be helpful?
 Trajectory is the unit of work for our
traffic management application
we partially use the trajectory model
developed (i.e. Stops-Moves)
◦ Stops have been already calculated and are
represented by an attribute for each trajectory
◦ Trajectory segmentation is constrainted by
road network segmentation
9. Our Dataset
 GPS Signals
 Street Network
◦ Street segmentation
◦ Street names
10. The Star Schema
Fact table entry:
A trajectory instance
11. Oracle OLAP SQL interface
 How to access MOLAP using SQL?
12. First Query: Spatial
 Which is the correlation between pollution
caused by high speed and congestions?
13. Second Query: Spatio-Temporal
 How Congestions are evolving during week?
14. Third Query: Numeric
 Which streets have globally the worst
Traffic Index Street
For the Overall Rio
AVN AMARO CAVALCANTE
27,886 ACESSO A PTE PRES COSTA E SILVA
ACESSO AVN GOVERN CARLOS LACERDA
ACESSO DO VTO DE MANGUINHOS
15. Remarks using Oracle OLAP
◦ Good Expressive Power for Aggregations
◦ Multi-dimensional representation
◦ SQL interface from MOLAP to Relational
◦ Too many Catalog tables!
◦ No robust bulk loading methods: Fatal Errors!
◦ Slow queries also with simple mapping to Relational
◦ To query a Cube with streets and Time dimension, it is
required 3-4 Mins.
◦ Limitations of supported types:
◦ Only TEXT, Number, Date
◦ No Complex Objects
 The design process is dangerous!
◦ Lack of Error Handling
 SQL interface leads to wider uses e.g. GIS
 Future work: use OLAP DML to enhance