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

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

8. Methods Imported Excel files to Access Caluclated TTI in Excel
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. Calculated Average TTI for each Day/Time to make graphs
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 Lack of data
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

18. Graph

19. Graph 19

20. Tuesday Standard Deviation20

21. Tuesday Standard Deviation21

22. Tuesday Standard Deviation22

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