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Los Angeles County Traffic Analysis

Los Angeles County Traffic Analysis. Geog 176c - Project Proposal. Project Advisor: Kirk Goldsberry Group Members: Tyler Brundage Cara Moore Art Eisberg David Fleishman AJ Block. Traffic. Objectives. Create a traffic atlas using empirical data

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Los Angeles County Traffic Analysis

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  1. Los Angeles County Traffic Analysis Geog 176c - Project Proposal Project Advisor: Kirk Goldsberry Group Members: Tyler Brundage Cara Moore Art Eisberg David Fleishman AJ Block

  2. Traffic

  3. Objectives • Create a traffic atlas using empirical data • Supplement perceptions of LA traffic

  4. Objectives • Depict traffic trends in LA using a GIS • Highlight problem areas and time periods • Create a straightforward representation for the general public

  5. Final Product

  6. Final Product http://www.geog.ucsb.edu/~ccm176/

  7. Methods • Outsourced MatLab scripting • Calculated Average Velocity with MatLab • Imported .txt files into Excel

  8. Methods • Imported Excel files to Access • Used Common Key to Link Velocities by Sensor ID number • Caluclated TTI in Excel • (Average Freeflow Velocity/ Average Velocity at Certain Time) • Exported file as a .dbf

  9. Travel Time Index • In layman’s terms, the TTI indicates how much longer a trip would take than it would in free-flow conditions • If TTI = 1, the trip would take the same amount of time as free flow traffic • If TTI = 2, the trip would take twice as long

  10. Methods • Calculated Average TTI for each Day/Time to make graphs • Merged Highways • Joined .dbf files to Highways

  11. Methods • Decided on Class Breaks/ Color Schemes • Created Maps in ArcMap • Created Flash File & Published Web Page

  12. Problems • Gaps in data • Data Spread • Excel prior to 2007 can only have 256 columns • Lack of data • Only used January

  13. Problems • Technical difficulties • -99s= non functioning sensors • TTI may not be intuitive

  14. Problems • Large amount of data • 1368 Rows • 205 Maps • 280,440 lines in Flash • 10 minutes to open Flash File • 1 ½ hour plus to export file

  15. Interpreting Results • There appears to be a definite trend of traffic throughout the day • Rush Hour • Northbound/Southbound & Eastbound/Westbound trends • However, there also appears to be many anomalies • Likely due to the spread of data used

  16. Graph

  17. Background Research * Y-axis indicates the fraction of sensors indicating congestion From http://home.znet.com/schester/calculations/traffic/la/index.html

  18. Graph

  19. Graph 19

  20. Tuesday Standard Deviation 20

  21. Tuesday Standard Deviation 21

  22. Tuesday Standard Deviation 22

  23. Interpreting Results • We have yet to test the efficiency of the final map • Therefore, we do not know how intuitive the final product is

  24. Conclusions • While a general trend associated with time and day can be seen, no conclusions should be made without a more in depth analysis using a larger data spread

  25. Future • Use more data to provide more conclusive results • More days • More times (e.g. every 5 minutes)

  26. Future • Test final product with general public • Based on public input, edit map to make it more user friendly

  27. Future • Develop Algorithm using TTI so services like MapQuest could provide time estimations from point A to point B

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