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Los Angeles County Traffic Analysis

This project aims to create a traffic atlas of LA using empirical data to highlight trends and problem areas, providing a straightforward representation for the public. Methods include MatLab scripting and data analysis to calculate Travel Time Index. Challenges include data gaps and technical difficulties. Results show traffic trends, but further analysis is needed for conclusive insights. Future steps include expanding data and user testing for improved usability.

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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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