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

Overview  Comparisons among cities: can you borrow your neighbor’s TOD model?  Peak spreading: a myth?  NHB sub-purposes: worthwhile?  Validation: it’s a good thing

Comparison Among Cities and Years  Atlanta & 2002  Baltimore  Charlotte  New Orleans  Reading, PA  Washington, DC & 1994

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

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

Assignment Periods AMMDPMNTOP Atlanta45411 Baltimore36312 Charlotte36312 New Orleans37311 Reading37311 Washington3318

Atlanta – 1995 – Work

Atlanta – 2002 – Work

Baltimore – Work

Charlotte – Work

New Orleans – Work

Reading – Work

Washington – 1989 – Work

Washington – 1994 – Work

Observations on Work Trips  Work trips are pretty regular  AM peak usually higher than PM peak  7:30 – 8:00 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

Atlanta – 1995 – All Trips

Atlanta – 2002 – All Trips

Baltimore – All Trips

Charlotte – All Trips

New Orleans – All Trips

Reading – All Trips

Washington – 1989 – All Trips

Washington – 1994 – All Trips

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

Peak Trip Share Declines AtlantaWashington Work6:30-9:30 a38%34%36%34% 3:30-6:30 p35%30%34%33% off-peak27%36%30%33% Other6:30-9:30 a14%9%12%15% 3:30-6:30 p24%23%39%25% off-peak62%68%49%60%

Peak Trip Share Declines – All Cities Work Trend Line Other Trend Line

Causes of Peak Spreading  Increased traffic congestion  Changing lifestyles  More flex-time  More part-time workers

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

Washington – 1994 – NHB JTW

Washington – 1994 – NHB JAW

Washington – 1994 – NHB NWK

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

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

Questions  Please use Microphone