Analysis of Time of Day Models from Various Urban Areas

1
Analysis of Time of Day Models from Various Urban
Areas

• William G. Allen, Jr.
• Transportation Planning Consultant
• Windsor, SC
• TRB Transportation Planning Applications
  Conference
• Daytona Beach, FL
• 9 May 2007

2
Overview

• Comparisons among cities can you borrow your
  neighbors TOD model?
• Peak spreading a myth?
• NHB sub-purposes worthwhile?
• Validation its a good thing

3
Comparison Among Cities and Years

• Atlanta - 1995 2002
• Baltimore - 2001
• Charlotte - 2000
• New Orleans - 2000
• Reading, PA - 1994
• Washington, DC - 1989 1994

4
TOD Analysis

• Data from home interview surveys, processed the
  same for each city
• 30 minute increments
• Separate by trip purpose and directionality
• Home to non-home and non-home to home
• Vehicle trips only
• Simple process for aggregate 4-step models

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Survey Processing

• Summary of trips by time in motion
• Compute reported vehicle-minutes by trip
• Tabulate veh-minutes by 30 min. period
• Get fraction of VHT by period
• Group 30 min. periods as desired for assignment
• Apply fractions to daily vehicle trip table

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Assignment Periods
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Atlanta 1995 Work
8
Atlanta 2002 Work
9
Baltimore Work
10
Charlotte Work
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New Orleans Work
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Reading Work
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Washington 1989 Work
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Washington 1994 Work
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Observations on Work Trips

• Work trips are pretty regular
• AM peak usually higher than PM peak
• 730 800 AM is the highest half-hour
  everywhere
• Some pattern differences are logical
• New Orleans tourist-based economy
• Reading shift workers
• Washington regular pattern of government workers

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Atlanta 1995 All Trips
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Atlanta 2002 All Trips
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Baltimore All Trips
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Charlotte All Trips
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New Orleans All Trips
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Reading All Trips
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Washington 1989 All Trips
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Washington 1994 All Trips
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Observations on All Trips

• Atlanta Washington, over time
• Peaks get lower midday/night higher
• People leave earlier, return later
• Comparisons among cities
• Washington Baltimore similar
• PM high, AM less peaked
• Atlanta, Charlotte, Reading (?!) similar
• New Orleans unique

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Peak Trip Share Declines
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Peak Trip Share Declines All Cities
Work Trend Line
Other Trend Line
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Causes of Peak Spreading

• Increased traffic congestion
• Changing lifestyles
• More flex-time
• More part-time workers

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Non-Home-Based Sub-purposes

• A fast-growing trip category
• NHB categories based on tour type
• JTW home-other-work or work-other-home
• JAW work-other-work
• NWK home-other-other-other-home
• Trip generation and distribution are similar
• TOD is very different

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Washington 1994 NHB JTW
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Washington 1994 NHB JAW
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Washington 1994 NHB NWK
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Validation

• Compare link volumes to counts by assignment
  period
• This type of TOD model sometimes overestimates
  peak period volumes
• Reduce peak fractions, increase off-peak
  fractions until volumes counts
• A very necessary step
• Difficult to get counts
• Easy to adjust fractions

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Conclusions

• Work trip patterns are generally consistent
• Some peak spreading over time
• Increased congestion is part of the reason
• Splitting NHB into sub-purposes is important for
  TOD
• This approach quickly produces a usable TOD
  model, but validation is important
• TOD models are not really transferable

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Questions

• Please use Microphone