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Application of Time-of Day Choice Models Using EMME/2

Application of Time-of Day Choice Models Using EMME/2. Transportation leadership you can trust. Washington State DOT Congestion Relief Analysis. presented to 19 th International EMME/2 Users’ Conference

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Application of Time-of Day Choice Models Using EMME/2

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  1. Application of Time-of Day Choice Models Using EMME/2 Transportation leadership you can trust. Washington State DOT Congestion Relief Analysis presented to19th International EMME/2 Users’ Conference presented byArun Kuppam, Cambridge SystematicsMaren Outwater, Cambridge Systematics, Inc.Mark Bradley, MBRCLarry Blain, PSRCRobert Tung, RST InternationalShuming Yan, WSDOT October 19, 2005Seattle, Washington

  2. Project Objectives • To capture variations in time of day by 30-minute time periods • To develop an approach that is sensitive to pricing scenarios • To capture travel behavior that reflects tendency to shift to nearby time periods

  3. Shortcomings of previous TOD Model • Five discrete time periods • Model calibration based on unweighted survey • Variation by income groups not captured

  4. Characteristics of New TOD Model • A logit time of day choice model, applied after mode choice to auto trips • 32 time periods – half hours except first and last periods • Variables include demographics, trip characteristics (carpool, bridge crossing), delay • Includes costs measured in units of time • Use of non-linear “shift” variable within 3 larger time periods

  5. Time Periods • AM Peak – 10 30-minute time periods from 5:00 a.m. to 10:00 a.m. • Midday – 10 30-minute time periods from 10:00 a.m. to 3:00 p.m. • PM Peak – 10 30-minute time periods from 3:00 p.m. to 8:00 p.m. • Evening – 1 3-hour time period from 8:00 p.m. to 11:00 p.m. • Night – 1 6-hour time period from 11:00 p.m. to 5:00 a.m.

  6. Model Specification • Multinomial Logit Structure with 32 alternatives • U = ASC + C1*(Delay) + C2*[(Delay.min.20 + sqrt(Delay–20).max.0)*Shift] + C3*[(Delay.min.20 + sqrt(Delay–20).max.0)*(Shift^2)] + C4*(Bridge Dummy) + C5*(Bridge Dummy*Shift) + C6*(Carpool Dummy) + C7*(Carpool Dummy*Shift) + C8*(Household Size) + C9*(Household Size*Shift) + C10*(Income Group) + C11*(Income Group*Shift) • Where, Delay for AM = max[(AM GC – NI GC), 0] Shift ‘Early’ for AM = (7.5 – T) Shift ‘Later’ for AM = (T – 7.5) T = Hour – 1, 2, 3, ……, 24 Bridge Dummy = 1 or 0 Carpool Dummy = 1 or 0 Household Size = min (hhsize, 4) Income Group = <$45k, >$75k

  7. TOD Modeling System SOV, HOV2, and HOV3+ Trip Tables by Time Period (32) Auto Trip Tables by Occupancy and Purpose Time-of-DayChoice Model Walk and Drive Access Transit Trip Tables by Time Period (5) Transit Trip Tables for Walk and Drive Access Time-of-DayPeaking Factor Model Light-, Medium-, and Heavy-Truck Trip Tables by Time Period (5) Commercial Vehicle and External Trip Tables SummaryReports Legend: Input Files Models/Processes Report Output Files Data Output Files

  8. Probabilities from TOD Model ApplicationHome to Work Home to Work TOD Distribution as a Function of AM Peak Delay 0 min Probability 5 min 0.2 0.18 10 min 0.16 15 min 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 Night Evening 0500-0529 0530-0559 0600-0629 0630-0659 0700-0729 0730-0759 0800-0829 0830-0859 0900-0929 0930-0959 1000-1029 1030-1059 1100-1129 1130-1159 1200-1229 1230-1259 1300-1329 1330-1359 1400-1429 1430-1459 1500-1529 1530-1559 1600-1629 1630-1659 1700-1729 1730-1759 1800-1829 1830-1859 1900-1929 1930-1959 Time of Day

  9. Probabilities from TOD Model ApplicationWork to Home Work to Home TOD Distribution as a Function of PM Peak Delay 0 min Probability 5 min 0.18 10 min 0.16 0.14 15 min 0.12 0.1 0.08 0.06 0.04 0.02 0 Night Evening 1030-1059 1100-1129 1130-1159 1200-1229 1230-1259 1300-1329 1330-1359 1400-1429 1430-1459 1500-1529 1530-1559 1600-1629 1630-1659 1700-1729 0930-0959 1000-1029 1730-1759 1800-1829 0500-0529 0530-0559 0700-0729 0730-0759 0800-0829 0830-0859 0900-0929 1830-1859 1900-1929 1930-1959 0600-0629 0630-0659 Time of Day

  10. Probabilities from TOD Model ApplicationHBW Drive Alone Trips – Variation by Income Group and Direction Shares of Trips 0.200000 A-P Inc1 A-P Inc2 A-P Inc3 A-P Inc4 P-A Inc1 P-A Inc2 0.180000 P-A Inc3 P-A Inc4 0.160000 0.140000 0.120000 0.100000 0.080000 0.060000 0.040000 0.020000 - 8:30 9:00 9:30 5:00 5:30 6:00 6:30 7:00 7:30 8:00 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 17:00 17:30 18:00 18:30 19:00 19:30 15:30 16:00 16:30 Time of Day

  11. Validation Results • Two-stage Validation • Stage 1 – Validate TOD shares by trip purpose, mode of travel, and direction, results within +/- 0.02 • Stage 2 –Validate VMT against traffic counts by TOD, results within +/- 10%

  12. Conclusions • Time-of-day choice models can be estimated with 30+ time periods with existing data • Models are sensitive to time and cost tradeoffs, as well as demographic factors and bridge constraints • Calibration by mode, trip purpose, and direction, as well as for volumes provides more behavioral understanding of results • Initial sensitivity tests indicate that models produce reasonable results

  13. Acknowledgements • Project was completed in support of model improvements for • Washington State Department of Transportation • Puget Sound Regional Council • Expert Review Panel requested additional detail on time periods • University of Wisconsin, Milwaukee, WI • North Central Texas Council of Governments, Dallas, TX • Portland Metro, Portland, OR • Sound Transit, Seattle, WA • Atlanta Regional Commission, Atlanta, GA

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