Implementing the FHWA Implementing the FHWA Quick Response Freight Quick Response Freight Model in the Twin CitiesModel in the Twin Cities

Steve Wilson and Jonathan EhrlichSteve Wilson and Jonathan Ehrlich SRF Consulting Group, Inc.SRF Consulting Group, Inc.

Dan BeaganDan BeaganCambridge Systematics, Inc.Cambridge Systematics, Inc.

Presented to Tenth Transportation Research BoardPresented to Tenth Transportation Research Board Planning Applications ConferencePlanning Applications Conference

Twin Cities Regional Twin Cities Regional Freight Planning ModelFreight Planning Model

ObjectivesObjectives

Approach/MethodologyApproach/Methodology

Calibration ConsiderationsCalibration Considerations

ApplicationsApplications

Objectives of the ProjectObjectives of the Project

Improve New Regional Travel Improve New Regional Travel Demand ModelsDemand Models

Better Reflect Impact of Trucks on Better Reflect Impact of Trucks on Traffic FlowTraffic Flow

Lay Groundwork for Multimodal Lay Groundwork for Multimodal Freight Analysis and ModelingFreight Analysis and Modeling

Connect to Statewide Freight PlanConnect to Statewide Freight Plan

ApproachApproach

Based on FHWA Based on FHWA Quick Quick Response Freight Response Freight ManualManual (1996) (1996)

Make use of local Make use of local datadata

Make use of limited Make use of limited resourcesresources

Zonal DataZonal Data

• Employment Employment Categories:Categories:

Agriculture, Mining, Agriculture, Mining, ConstructionConstruction

Manufacturing, Manufacturing, Transportation, Transportation, Communication, Communication, Utilities, Wholesale Utilities, Wholesale TradeTrade

Retail TradeRetail Trade Office and ServicesOffice and Services

Zonal DataZonal Data

Employment Employment DataData• Census vs. Census vs.

ES202ES202 Freight Facility Freight Facility

InventoryInventory• Quality Control Quality Control

IssuesIssues

Network DataNetwork Data

HighwaysHighways• ProhibitionsProhibitions• GradeGrade• Vehicle Classification CountsVehicle Classification Counts

Navigable WaterwaysNavigable Waterways• Terminal LocationsTerminal Locations• Locks and DamsLocks and Dams

RailwaysRailways • At-Grade CrossingsAt-Grade Crossings• Intermodal TerminalsIntermodal Terminals

Other DataOther Data

Freight Analysis FrameworkFreight Analysis Framework• National Freight Flow DatabaseNational Freight Flow Database• Growth Rates for External TripsGrowth Rates for External Trips

External Origin-Destination External Origin-Destination SurveySurvey• Distribution of External TripsDistribution of External Trips

Other DataOther Data

Light Commercial VehiclesLight Commercial Vehicles• In the regional model (large portion of In the regional model (large portion of

NHBW trips)NHBW trips)• Difficult to quantify either in vehicle Difficult to quantify either in vehicle

classification counts or in home-classification counts or in home-interview surveysinterview surveys

• MnDOTMnDOT Light Commercial Vehicle Light Commercial Vehicle Study,Study, 1999 1999

Four Step Model: Trip Four Step Model: Trip GenerationGeneration

By Vehicle Classification from By Vehicle Classification from QRFMQRFM• Single UnitSingle Unit• CombinationCombination• ‘‘Light Commercial’Light Commercial’

Function of Employment TypeFunction of Employment Type External Trips based on External External Trips based on External

Station Vehicle ClassificationStation Vehicle Classification Special Generator CapabilitySpecial Generator Capability

Four Step Model: Trip Four Step Model: Trip DistributionDistribution

Default gravity model friction factor Default gravity model friction factor curves from curves from QRFMQRFM

Modify trip table using matrix Modify trip table using matrix estimation estimation

Adjusted default curves based on Adjusted default curves based on estimated matrixestimated matrix

Figure 3: Single Unit Truck Trip Length Distribution

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0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Off-Peak Congested Travel Tim e

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f T

rip

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Estimated Distribution Model

Four Step Model: Time of Four Step Model: Time of DayDay

Matches 24 time Matches 24 time periods from periods from Regional ModelRegional Model

Constant factors for Constant factors for each time period for each time period for each vehicle each vehicle classificationclassification

Based on 24-hour Based on 24-hour vehicle classification vehicle classification count datacount data

Four Step Model: Four Step Model: AssignmentAssignment

Regional Model AssignmentRegional Model Assignment• Equilibrium AssignmentEquilibrium Assignment• 24 Time Periods24 Time Periods

Five Assignment PurposesFive Assignment Purposes• SOV, LCV, HOV, SU, CMBSOV, LCV, HOV, SU, CMB

Three PathsThree Paths• SOV/LCV, HOV, TruckSOV/LCV, HOV, Truck

Passenger Car EquivalencyPassenger Car Equivalency

ValidationValidation

Highway Link VolumesHighway Link Volumes

VMTVMT

Calibration ConsiderationsCalibration Considerations

Data AvailabilityData Availability• Vehicle ClassificationVehicle Classification• Internal Origin-DestinationInternal Origin-Destination• Trip Length DistributionTrip Length Distribution• External Station DataExternal Station Data• Commodity DataCommodity Data

Concurrent Regional Model Concurrent Regional Model CalibrationCalibration

Regional ScopeRegional Scope

ApplicationsApplications

Forecasting Truck TrafficForecasting Truck Traffic• Pavement DesignPavement Design• Development ImpactsDevelopment Impacts

System Level ResponsesSystem Level Responses• Modal Shift Impacts Modal Shift Impacts • Value of Time/Benefit-CostValue of Time/Benefit-Cost

Truck Impacts on CongestionTruck Impacts on Congestion Grade Crossing AnalysisGrade Crossing Analysis

Recommendations for Further Recommendations for Further ImprovementImprovement

Commercial Vehicle Tracking SurveyCommercial Vehicle Tracking Survey

National/Multi-State Commodity Flow National/Multi-State Commodity Flow ModelModel

External Truck Intercept SurveyExternal Truck Intercept Survey

Vehicle Classification DataVehicle Classification Data

Questions?Questions?