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Revolutionizing Truck Model Forecasting in National Capital Region

Explore the innovative approach to truck model development and validation in the National Capital Region, transforming forecasting accuracy and reliability for commercial, medium, and heavy trucks. Learn about the comprehensive process, key findings, and future projections for truck trips and vehicle miles traveled. Stay ahead with cutting-edge methodologies to ensure optimized goods movement planning.

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Revolutionizing Truck Model Forecasting in National Capital Region

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  1. New Truck Models National Capital Region TPB Travel Forecasting Subcommittee William G. Allen, Jr., P.E. Transportation Planning Consultant 18 July 2008

  2. Timeline • Feb. 04 – Apr. 07: COM model developed • 18 May 2007: COM presentation to TFS • May 2007: truck models started • Jan. 2008: truck models completed • Jan. – Jul. 08: TPB truck model testing • 23 May 2008: TPB truck presentation to TFS

  3. Vehicle Types • Commercial: Light-duty vehicle (car, van, pickup) used for non-personal transportation • Medium Truck: 2 axles, 6 tires (FHWA class 5) • Heavy Truck: 3+ axles (FHWA classes 4 and 6-13) • Includes Buses

  4. Definitions • Heavy Truck: 3+ axles, more than 6 tires • Medium Truck: 2 axle, 6 tires • Commercial: light duty vehicles used for business

  5. Truck model history • Original model developed from 1968 truck O-D survey • Original truck types: light, medium, heavy • Models updated in 1985, based on limited count data and 1968 survey

  6. A new approach • Truck surveys don’t work • Use new method developed for COM model • Trip-based method consistent with current modelling procedures • Borrow a starting model • Use counts to synthesize more counts • Use counts to adjust starting trips • Use trip difference to refine the starting model • Same method used for BMC, ARC, Ohio DOT

  7. Truck Counts • Primary data source: MDOT counts • 6 permanent counts • 315 short-term counts • Secondary data sources: • DC classification counts (11) • Virginia classification counts (33) • TPB 4-hour class. counts (148) • (2003) TPB External Truck Survey (10 sites)

  8. Daily truck count statistics • Truck percentage: MTK=3.3%, HTK=3.2% • TRK % range: 0.9% to 26.1% (US 1 in Jessup, MD) • TRK vol. range: 27 – 14,700 (I-95, Howard Co.) • Approx. TOD split: 19% AM, 14% PM, 67% OP

  9. Synthesized Counts • Model % HTK, % MTK by link using count data • Logit function: % TRK = 1 / (1 + eU) • U  lanes, facility type, area type, jurisdiction • Apply to all links with counts • TRK “count” = est % TRK * count • Thorough manual review • Use actual counts where available

  10. % TRK analysis findings • Synthesizing counts provides data for DC and VA • % TRK goes up with: • Less developed areas • Higher facility types • Increasing lanes (MTK); decreasing lanes (HTK)

  11. Borrow a starting model • Simple linear regression model, from BMC model • Based on employment by type (industrial, office, retail, other) and HHs • Adjustments for area type, truck zone • F’s from BMC and Quick Response Freight Manual • TOD percentages from MDOT count data

  12. Truck Zones • Zones with identifiable truck generators • Business districts, warehouses, manufacturing, transfer, airport, delivery • 1/0 flag • 35 zones identified • 3-5 times higher truck trip rate per job

  13. Model Statistics • Retail, Industrial empl are most important • Higher trip rate (per empl) in less developed areas • External share  distance from cordon • 2005 trip totals: MTK=474 K, HTK=192 K, total=666 K • Avg. trip length: MTK=24 min., HTK=58 min., total=34 min. • Prior model: 553K trips, 43 min. avg

  14. “Adaptable Assignment” Starting model Starting trip table Assign trips Repeat (7 iter.) Skim loads & counts Final trip table Adjust trips Delta trip table Inform

  15. Delta analysis • Subtract starting trips from new trips • Analyze trip end summary of difference • Correlate with HH, employment • Use to inform model (revise coeffs.) • Keep “delta” table as adjustment

  16. Calibration adjustment • O/D table of mostly small adjustments • Accounts for random error in assignment • Table totals: MTK=7.8 K, HTK=25.7 K • Tend to be short trips; no other pattern • Carried along for forecasting, added to model’s starting trip table

  17. Validation results (2005) • Prior model (v2.1D, fall 2005) • % RMSE: 104%, volume/count: 1.16 • 553 K trips • Starting model • % RMSE: 111%, volume/count: 1.33 • 632 K trips • Final model • % RMSE: 51%, volume/count: 1.07 • 666 K trips

  18. More results • 2005 VMT • Current: 11.4 M • New: 10.8 M (-5%) • Trips go up, average length goes down • 2030 estimate • Trips: 916 K (+38%) • VMT: 17.3 M (+60%)

  19. Conclusions • TPB wanted a cost-effective, practical, proven approach • Goods movement approach not ready • Truck trips are complex -- not suited to an aggregate four-step approach • Truck travel is related to national policy and macroeconomic factors beyond our knowledge • Don’t wait 20 years to revisit the truck models

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