Travel Demand Model - OKI

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OKI/MVRPC Travel Demand Model – Version 6.0Table 3.7. Estimated vs. Observed Daily Truck Volumes by Functional ClassDaily Truck Volume Number of Root Mean PercentFunctional Class Estimated Observed Observations Squared Error RMSE Est./Obs.1. Interstates 1,799,116 1,656,772 210 2,250 28.52 1.09I-75 Mainline 1,126,287 1,031,196 94 2,911 26.53 1.092. Major Arterials 192,787 155,172 203 557 72.86 1.243. Minor Arterials 141,456 121,721 156 628 80.54 1.164. Major Collectors 37,951 41,202 103 346 86.49 0.925. Minor Collectors 56 18 1 3.118. Ramps 93,931 123,975 179 524 75.63 0.769. Expressways 68,794 86,236 38 1,279 56.37 0.80Total Observations 2,334,091 2,185,096 890 1,211 49.32 1.07* Root mean squared error is not calculable for just one observation.A comparison of link counts by urban area type shows is shown in Table 3.8. While there are somedifferences, link-flow error measures are similar between area types. Links in CBD areas have the bestoverall PRMSE, while links in areas surrounding the CBD, designated as Urban, appear to be overpredicted.In terms of estimated to observed truck volumes, links in the Suburban area type comesclosest to a ratio of one.Table 3.8. Estimated vs. Observed Daily Truck Volumes by Coded Area TypesDaily Truck Volume Number of Root Mean PercentCoded Area Type Estimated Observed Observations Squared Error RMSE Est./Obs.1. CBD 49,491 47,007 25 573 30.48 1.052. Urban 1,007,571 877,980 403 1,263 57.96 1.153. Suburban 1,110,981 1,100,360 351 1,294 41.27 1.014. Rural 166,048 159,749 111 790 54.91 1.04Total Observations 2,334,091 2,185,096 890 1,211 49.32 1.07A comparison of estimated and observed truck volumes by county is shown in Table 3.9. As with areatype, the link-flow error measurements are similar across the region. Links in Boone and WarrenCounties show very good PRMSE and near-perfect estimated-to-observed ratios. Hamilton andMontgomery Counties also showed relatively good statistics.The two places where link-flow error measurements are of some concern are Miami and Kenton Counties.In Miami County, the estimated to observed ratio is a low 0.69; however, this should be viewed in light ofthe fact that all of the 54 counts used in Miami County were taken in 2000, whereas the majority ofcounts in neighboring Montgomery County were taken in 1994 and the external station survey was donein 1995 (see Table 2-2). Since the SME process will attempt to fit O-D flows to all counts, it should beexpected that the estimated truck volumes in Miami County would fall below the counted 2000-yearvolumes. From this perspective, the estimated-to-observed ratio of 0.69 is actually a more positivemeasure, given that the base-year model is supposed to represent 1995 conditions.In Kenton County, the problem is one of over-prediction, as evident by the estimated-to-observed ratio of1.28. This is also attributable to inconsistencies between count sources. The links contributing thelargest amount of flow error are along I-75. I-75 links to the north, in Cincinnati, and to the south inBoone, County, have relatively low link-flow errors. Although they are of similar count years, thedifference between the Kentucky ADT counts and the 12-hour-expanded counts used to calibrate themodel along I-75 in Cincinnati contributed to this discrepancy.Truck Model - Base-Year Truck Model Results 43
OKI/MVRPC Travel Demand Model – Version 6.0Table 3.9. Estimated vs. Observed Daily Truck Volumes by CountyDaily Truck Volume Number of Root Mean PercentCounty, State Estimated Observed Observations Square Error RMSE Est./Obs.Boone, KY 230,285 224,974 37 1,865 30.68 1.02Butler, OH 142,357 128,767 70 764 41.54 1.11Campbell, KY 40,251 46,424 21 824 37.29 0.87Clermont, OH 66,611 70,594 51 830 59.99 0.94Greene, OH 82,643 74,179 84 460 52.14 1.11Hamilton, OH 767,923 675,660 272 1,154 46.48 1.14Kenton, KY 196,926 154,130 33 2,726 58.37 1.28Miami, OH 100,462 146,510 54 2,010 74.10 0.69Montgomery, OH 528,640 485,859 187 1,067 41.07 1.09Warren, OH 177,993 177,999 81 766 34.87 1.00Total Observations 2,334,091 2,185,096 890 1,211 49.32 1.07Truck Model - Base-Year Truck Model Results 44
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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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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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2759OKI/MVRPC Travel Demand Model -
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Page 383 and 384: OKI/MVRPC Travel Demand Model - Ver
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