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Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
Trajectory Data Warehousing
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Trajectory Data Warehousing

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  • 1. A Study on Trajectory Data Aggregation Simone Campora February 4th 2009
  • 2. Introduction  The Purpose: ◦ Building a data warehouse for Rio de Janeiro Traffic Dpt ◦ Prototype a trajectory datawarehouse ◦ Explore DW potentialities  Challenges: ◦ 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 Population (2007) Municipality 7,145,472 Density 4,781/km2 Metro 13,782,000 HDI (2000) 0.842 – high Streets 13858  Some Facts:  Number of cars is mostly doubled during the last year!
  • 4. Problems  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 points/KM (50 km/h) Traffic Density 72 9 96 18 144 35 288 70 -> ∞ 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? (Spatio-Temporal) ◦ Q3: Which streets are the most congested? (Numeric) 1 2 3
  • 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 Note
  • 9. Our Dataset  GPS Signals ◦ Position ◦ Time ◦ Speed  Street Network ◦ Street segmentation ◦ Street names $GPRMC, €,V,2253.7009,S,04321.2711,W,,,,021.8,W,N*1C $GPGGA,,2253.7009,S,04321.2711,W,0,00,00.00,000012.8,M,-005.8,M,,*6E $GPZDA,103037,11,05,2007,+00,00*65 $GPRMC, €,V,2253.7009,S,04321.2711,W,,,,021.8,W,N*1C ... $GPRMC,103501,A,2300.0632,S,04319.8165,W,017.2,100.3,110507,021.8,W,A*0C $GPRMC,103502,A,2300.0642,S,04319.8120,W,015.1,103.3,110507,021.8,W,A*0B $GPRMC,103503,A,2300.0651,S,04319.8082,W,013.1,103.4,110507,021.8,W,A*00 Trajectories
  • 10. The Star Schema Time Streets Trajectories Fact table entry: A trajectory instance segment
  • 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 conditions? Traffic Index Street 25,131 For the Overall Rio 39,077 AVN AMARO CAVALCANTE 27,886 ACESSO A PTE PRES COSTA E SILVA 24,032 ACESSO AVN GOVERN CARLOS LACERDA (LINHA AMARELA) 15,651 ACESSO DO VTO DE MANGUINHOS
  • 15. Remarks using Oracle OLAP  Positives: ◦ Good Expressive Power for Aggregations ◦ Multi-dimensional representation ◦ SQL interface from MOLAP to Relational  Drawbacks ◦ 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
  • 16. Conclusions  The design process is dangerous! ◦ Lack of Error Handling  SQL interface leads to wider uses e.g. GIS tools  Future work: use OLAP DML to enhance running times
  • 17. Thanks for the Attention

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