Travel Demand Model - OKI

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OKI/MVRPC Travel Demand Model – Version 6.04.1.2 Develop industry-specific productivity deflation factorsThe purpose of this step is to reflect greater output (more truck trips) per employee over time.Experience in forecasting freight flows from employment data has shown that failure to account forchanges in productivity over time can result in a substantial under-prediction of truck traffic.Details: The productivity deflation factors for the NSTI project were developed from industrial outputdata produced for the Michigan Department of Transportation statewide freight model. Use of these dataspared the expense of obtaining equivalent data for the Cincinnati-Dayton region. They are appropriatefor the NSTI study because industries in Michigan have similar productivity characteristics to industries inOhio, Kentucky and Indiana.The data were expressed in terms of millions of chained dollars of output per employee by ten industryclassifications, representing the years 1995 to 2025, with five-year increments after 2000. Projectedindustry outputs per employee use constant (1992) dollars and reflect increased production rates, whichmay be attributed to the expected adoption of new technologies and improvements in operatingefficiency. Thus, the change in millions of dollars of output per employee between the base year and theforecast year is assumed to result in a proportional growth in truck trips per employee. For the NSTIstudy, the average annual growth rates in productivity from 1995 to 2025 were used to produce 2030productivity rates, as shown in Figure 4.2.0.500Millions of 1992 dollars per worker0.4500.4000.3500.3000.2500.2000.1500.1000.050Durable Manufg.Non-durable Manufg.MiningConstructionTransp, Comm., Util.FIRERetailWholesaleServicesAgric., Fish., Forest.0.0001995 2000 2005 2010 2015 2020 2025 2030YearFigure 4.2. Expected growth in industrial productivity, 1995 to 2030Table 4.2, below, shows the forecasted deflation factors corresponding to productivity growth over the35-year planning horizon, calculated as the ratio of 2030 output per worker to 1995 output per worker,by industry category. Very large productivity growth ratios are predicted for the manufacturing andwholesale trade sectors, while the agriculture, fishing and forestry sector is expected to show no changein productivity.Truck Model - Truck Trip Table Forecasting Procedures 47
OKI/MVRPC Travel Demand Model – Version 6.0Table 4.2. Industry sector deflation, 1995 to 2030Industry SectorRatio: 2030 / 1995output per workerDurable Manufacturing 2.650Non-durable Manufacturing 1.900Wholesale Trade 1.806Finance, Insurance & Real Estate (FIRE) 1.593Mining 1.472Transportation, Communications & Utilities 1.421Services 1.215Retail Trade 1.203Construction 1.176Agriculture, Fishing & Forestry 1.000To form deflation factors for the four QRFM truck trip generation categories, weighted averages of theten industry categories shown in Table 4.2 were calculated for each FAZ, based on the distributions ofsector employment found in the 2000 employment obtained from Claritas, Inc. The deflation factorcalculations for a single FAZ i would beDAMCi1.000* E=AgricultureiAgricultureEi+ 1.472* E+ EMiningiMiningi++ 1.176* EEConstructioniConstructioniwhereDAMCiis the deflation factor corresponding to the QRFM truck trip generation coefficient for“agriculture, mining and construction” employment in FAZ i.sector;EMiningiis employment in the mining sector; andAgricultureEiis employment in the agriculturalConstructionEiis employment in the constructionsector. The numerical values beside each employment variable are the industry-sector deflation factorsfrom Table 4.2.The deflation factor calculations for other QRFM trip generation categories were calculated asDMTUWiRetailDi2.650* E+ 1.900* E+ 1.421* EDurableMfgNondurableMfgTransp&Utilitiesiii=DurableMfg NondurableMfg Transp&Utilities WholesaleEi+ Ei+ Ei+ Ei= 1.203+ 1.806* EWholesaleiTruck Model - Truck Trip Table Forecasting Procedures 48
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APPENDIX F • TRAVEL DEMAND MODEL
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OKI/MVRPCTravel Demand ModelVersion
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Table of ContentsPart IPart IIPart
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OKI/MVRPC Travel Demand Model - Ver
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Table A.1 (cont.)Zone System Equiva
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Table A.2Zone System Equivalences -
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Table A.3Zone System Equivalences -
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Table B.1External Stations in the C
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Table B.1External Stations in the C
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User Documentation forAutomated Acc
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[Transit Format][New Link Attribute
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distance for each centroid or node.
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Figure 1Example Control File for th
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LinkType = 99VolumeDelay = 99Lanes
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NetworkScale = 1.0 Scale factor to
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Figure 4Example Output from DRVLINK
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Appendix ASample EMME/2 Transit Lin
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Appendix FTravel Demand ModelingTec
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Index of TablesTable 2-1 Socio-Econ
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Appendix FTravel Demand ModelingTec
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Index of TablesTable 2-1 Trip Lengt
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Table of Contents1. Background ....
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Index of FiguresFigure 2-1 Alternat
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NORTH-SOUTH TRANSPORTATION INITIATI
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Run # 63: Start with 7.17 estimatio
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Run # 67:Split parking cost from ot
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Run # 70a:Include zero-auto segment
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Run # 71c:Repeat #71a with zero aut
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Run # 71g:Repeat #71d with wait tim
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Run # 73:Include residential densit
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Run # 76:Nest: auto vs transitVaria
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Run # 79:Repeat #72 with zero autos
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Run # 80:Nest: auto vs. all othersV
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Run # 82:(071h) nested: auto vs tra
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Run #3VariableModeDrive Shared Ride
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Run #8VariableModeDrive Shared Ride
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Run #12 test: make aoc=10.5 instead
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Run #29VariableModeDrive Shared Rid
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Run #33VariableModeDrive Shared Rid
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Run #37vot constrained to $7.00Vari
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Run #30bSame as #30a, combine cost
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Run #140 (same as run #40)vot const
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Run #140b (same as run #40)vot cons
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Run #141avot constrained to $3.00 &
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Run #141cvot constrained to $3.00 &
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Run # 1:Base RunVariablesModesDA Sh
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Run # 5:Base Run with walk time set
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Run # 8:Base Run, split walkVariabl
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Run # 10: Nested structure (autos v
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Part VITRIP ASSIGNMENT ANDVALIDATIO
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Index of TablesTable 2-1 Average Ve
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VOBFigure 4-8 Screenline Location -
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Page 387 and 388: OKI/MVRPC Travel Demand Model - Ver
Page 391 and 392: Table of Contents1. Introduction...
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Page 451 and 452: 2. Surveys and Other Datasets2.1 OK
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