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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
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Traffic
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Objectives Create a traffic atlas using empirical data
Supplement perceptions of LA traffic
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Objectives Depict traffic trends in LA using a GIS
Highlight problem areas and time periods Create a straightforward representation for the general public
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Final Product
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Final Product
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Methods Outsourced MatLab scripting Calculated Average Velocity with MatLab Imported .txt files into Excel
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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
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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
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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
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Methods Decided on Class Breaks/ Color Schemes Created Maps in ArcMap
Created Flash File & Published Web Page
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Problems Gaps in data Data Spread Lack of data
Excel prior to 2007 can only have 256 columns Lack of data Only used January
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Problems Technical difficulties -99s= non functioning sensors
TTI may not be intuitive
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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
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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
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Graph
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Background Research * Y-axis indicates the fraction of sensors indicating congestion From
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Graph
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Graph 19
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Tuesday Standard Deviation
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Tuesday Standard Deviation
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Tuesday Standard Deviation
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Interpreting Results We have yet to test the efficiency of the final map Therefore, we do not know how intuitive the final product is
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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
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Future Use more data to provide more conclusive results More days
More times (e.g. every 5 minutes)
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Future Test final product with general public
Based on public input, edit map to make it more user friendly
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Future Develop Algorithm using TTI so services like MapQuest could provide time estimations from point A to point B
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