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1
New Truck ModelsNew Truck Models
National Capital Region TPB
Travel Forecasting Subcommittee
William G. Allen, Jr., P.E.Transportation Planning Consultant
18 July 2008
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TimelineTimeline
• Feb. 04 – Apr. 07: COM model Feb. 04 – Apr. 07: COM model developeddeveloped
• 18 May 2007: COM presentation to TFS18 May 2007: COM presentation to TFS
• May 2007: truck models startedMay 2007: truck models started
• Jan. 2008: truck models completedJan. 2008: truck models completed
• Jan. – Jul. 08: TPB truck model testingJan. – Jul. 08: TPB truck model testing
• 23 May 2008: TPB truck presentation 23 May 2008: TPB truck presentation to TFSto TFS
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Vehicle TypesVehicle Types
• Commercial: Light-duty vehicle (car, Commercial: Light-duty vehicle (car, van, pickup) used for non-personal van, pickup) used for non-personal transportationtransportation
• Medium Truck: 2 axles, 6 tires Medium Truck: 2 axles, 6 tires (FHWA class 5)(FHWA class 5)
• Heavy Truck: 3+ axles (FHWA Heavy Truck: 3+ axles (FHWA classes 4 and 6-13)classes 4 and 6-13)– Includes BusesIncludes Buses
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DefinitionsDefinitions
• Heavy Truck: 3+ axles, Heavy Truck: 3+ axles, more than 6 tiresmore than 6 tires
• Medium Truck: 2 axle, Medium Truck: 2 axle, 6 tires6 tires
• Commercial: light duty Commercial: light duty vehicles used for vehicles used for businessbusiness
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Truck model historyTruck model history
• Original model developed from 1968 Original model developed from 1968 truck O-D surveytruck O-D survey
• Original truck types: light, medium, Original truck types: light, medium, heavyheavy
• Models updated in 1985, based on Models updated in 1985, based on limited count data and 1968 surveylimited count data and 1968 survey
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A new approachA new approach• Truck surveys don’t workTruck surveys don’t work
• Use new method developed for COM modelUse new method developed for COM model
• Trip-based method consistent with current Trip-based method consistent with current modelling proceduresmodelling procedures
• Borrow a starting modelBorrow a starting model
• Use counts to synthesize more countsUse counts to synthesize more counts
• Use counts to adjust starting tripsUse counts to adjust starting trips
• Use trip difference to refine the starting Use trip difference to refine the starting modelmodel
• Same method used for BMC, ARC, Ohio DOTSame method used for BMC, ARC, Ohio DOT
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Truck CountsTruck Counts
• Primary data source: MDOT countsPrimary data source: MDOT counts– 6 permanent counts6 permanent counts
– 315 short-term counts315 short-term counts
• Secondary data sources:Secondary data sources:– DC classification counts (11)DC classification counts (11)
– Virginia classification counts (33)Virginia classification counts (33)
– TPB 4-hour class. counts (148)TPB 4-hour class. counts (148)
– (2003) TPB External Truck Survey (10 (2003) TPB External Truck Survey (10 sites) sites)
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Daily truck count statisticsDaily truck count statistics
• Truck percentage: MTK=3.3%, Truck percentage: MTK=3.3%, HTK=3.2%HTK=3.2%
• TRK % range: 0.9% to 26.1% (US 1 TRK % range: 0.9% to 26.1% (US 1 in Jessup, MD)in Jessup, MD)
• TRK vol. range: 27 – 14,700 (I-95, TRK vol. range: 27 – 14,700 (I-95, Howard Co.)Howard Co.)
• Approx. TOD split: 19% AM, 14% Approx. TOD split: 19% AM, 14% PM, 67% OPPM, 67% OP
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Synthesized CountsSynthesized Counts• Model % HTK, % MTK by link using Model % HTK, % MTK by link using
count datacount data
• Logit function: % TRK = 1 / (1 + eLogit function: % TRK = 1 / (1 + eUU))
• U U lanes, facility type, area type, lanes, facility type, area type, jurisdictionjurisdiction
• Apply to all links with countsApply to all links with counts
• TRK “count” = est % TRK * countTRK “count” = est % TRK * count
• Thorough manual reviewThorough manual review
• Use actual counts where availableUse actual counts where available
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% TRK analysis findings% TRK analysis findings
• Synthesizing counts provides data Synthesizing counts provides data for DC and VAfor DC and VA
• % TRK goes up with:% TRK goes up with:– Less developed areasLess developed areas
– Higher facility typesHigher facility types
– Increasing lanes (MTK); decreasing Increasing lanes (MTK); decreasing lanes (HTK)lanes (HTK)
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Borrow a starting modelBorrow a starting model
• Simple linear regression model, from Simple linear regression model, from BMC modelBMC model
• Based on employment by type Based on employment by type (industrial, office, retail, other) and HHs(industrial, office, retail, other) and HHs
• Adjustments for area type, truck zoneAdjustments for area type, truck zone
• F’s from BMC and Quick Response F’s from BMC and Quick Response Freight ManualFreight Manual
• TOD percentages from MDOT count dataTOD percentages from MDOT count data
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Truck ZonesTruck Zones
• Zones with identifiable truck generatorsZones with identifiable truck generators
• Business districts, warehouses, Business districts, warehouses, manufacturing, transfer, airport, manufacturing, transfer, airport, deliverydelivery
• 1/0 flag1/0 flag
• 35 zones identified35 zones identified
• 3-5 times higher truck trip rate per job3-5 times higher truck trip rate per job
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Model StatisticsModel Statistics• Retail, Industrial empl are most importantRetail, Industrial empl are most important
• Higher trip rate (per empl) in less Higher trip rate (per empl) in less developed areasdeveloped areas
• External share External share distance from cordon distance from cordon
• 2005 trip totals: MTK=474 K, HTK=192 K, 2005 trip totals: MTK=474 K, HTK=192 K, total=666 Ktotal=666 K
• Avg. trip length: MTK=24 min., HTK=58 Avg. trip length: MTK=24 min., HTK=58 min., total=34 min.min., total=34 min.
• Prior model: 553K trips, 43 min. avgPrior model: 553K trips, 43 min. avg
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““Adaptable Assignment”Adaptable Assignment”
Assigntrips
Skim loads& counts
Adjusttrips
Finaltrip table
Deltatrip table
Repeat
(7 iter.)
Startingtrip table
Startingmodel
Inform
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Delta analysisDelta analysis
• Subtract starting trips from new tripsSubtract starting trips from new trips
• Analyze trip end summary of Analyze trip end summary of differencedifference
• Correlate with HH, employmentCorrelate with HH, employment
• Use to inform model (revise coeffs.)Use to inform model (revise coeffs.)
• Keep “delta” table as adjustmentKeep “delta” table as adjustment
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Calibration adjustmentCalibration adjustment
• O/D table of mostly small adjustmentsO/D table of mostly small adjustments
• Accounts for random error in Accounts for random error in assignmentassignment
• Table totals: MTK=7.8 K, HTK=25.7 KTable totals: MTK=7.8 K, HTK=25.7 K
• Tend to be short trips; no other patternTend to be short trips; no other pattern
• Carried along for forecasting, added to Carried along for forecasting, added to model’s starting trip tablemodel’s starting trip table
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Validation results (2005)Validation results (2005)
• Prior model (v2.1D, fall 2005)Prior model (v2.1D, fall 2005)– % RMSE: 104%, volume/count: 1.16% RMSE: 104%, volume/count: 1.16
– 553 K trips 553 K trips
• Starting modelStarting model– % RMSE: 111%, volume/count: 1.33% RMSE: 111%, volume/count: 1.33
– 632 K trips632 K trips
• Final modelFinal model– % RMSE: 51%, volume/count: 1.07% RMSE: 51%, volume/count: 1.07
– 666 K trips666 K trips
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More resultsMore results
• 2005 VMT2005 VMT– Current: 11.4 MCurrent: 11.4 M
– New: 10.8 M (-5%)New: 10.8 M (-5%)
– Trips go up, average length goes downTrips go up, average length goes down
• 2030 estimate2030 estimate- Trips: 916 K (+38%)Trips: 916 K (+38%)
- VMT: 17.3 M (+60%)VMT: 17.3 M (+60%)
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ConclusionsConclusions• TPB wanted a cost-effective, practical, proven TPB wanted a cost-effective, practical, proven
approachapproach
• Goods movement approach not readyGoods movement approach not ready
• Truck trips are complex -- not suited to an Truck trips are complex -- not suited to an aggregate four-step approachaggregate four-step approach
• Truck travel is related to national policy and Truck travel is related to national policy and macroeconomic factors beyond our macroeconomic factors beyond our knowledgeknowledge
• Don’t wait 20 years to revisit the truck modelsDon’t wait 20 years to revisit the truck models