Transcript Implementing the FHWA Quick Response Freight Model in the Twin

Implementing the FHWA Quick
Response Freight Model in the
Twin Cities
Steve Wilson and Jonathan Ehrlich
SRF Consulting Group, Inc.
Dan Beagan
Cambridge Systematics, Inc.
Presented to Tenth Transportation Research Board
Planning Applications Conference
Twin Cities Regional Freight
Planning Model

Objectives

Approach/Methodology

Calibration Considerations

Applications
Objectives of the Project




Improve New Regional Travel Demand
Models
Better Reflect Impact of Trucks on Traffic
Flow
Lay Groundwork for Multimodal Freight
Analysis and Modeling
Connect to Statewide Freight Plan
Approach

Based on FHWA Quick
Response Freight Manual


(1996)
Make use of local data
Make use of limited
resources
Zonal Data
• Employment
Categories:




Agriculture, Mining,
Construction
Manufacturing,
Transportation,
Communication,
Utilities, Wholesale
Trade
Retail Trade
Office and Services
Zonal Data

Employment Data
• Census vs. ES202

Freight Facility
Inventory
• Quality Control
Issues
Network Data

Highways
• Prohibitions
• Grade
• Vehicle Classification Counts

Navigable Waterways
• Terminal Locations
• Locks and Dams

Railways
• At-Grade Crossings
• Intermodal Terminals
Other Data

Freight Analysis Framework
• National Freight Flow Database
• Growth Rates for External Trips

External Origin-Destination Survey
• Distribution of External Trips
Other Data

Light Commercial Vehicles
• In the regional model (large portion of
NHBW trips)
• Difficult to quantify either in vehicle
classification counts or in home-interview
surveys
• MnDOT Light Commercial Vehicle Study,
1999
Four Step Model: Trip Generation

By Vehicle Classification from QRFM
• Single Unit
• Combination
• ‘Light Commercial’



Function of Employment Type
External Trips based on External Station
Vehicle Classification
Special Generator Capability
Four Step Model: Trip Distribution



Default gravity model friction factor curves
from QRFM
Modify trip table using matrix estimation
Adjusted default curves based on
estimated matrix
Figure 3:
Single Unit Truck Trip Length Distribution
Estimated
Distribution Model
6
Percent of Trips
5
4
3
2
1
0
0
5
10
15
20
25
30
35
40
45
50
Off-Peak Congested Travel Tim e
55
60
65
70
75
Four Step Model: Time of Day



Matches 24 time
periods from Regional
Model
Constant factors for
each time period for
each vehicle
classification
Based on 24-hour
vehicle classification
count data
Four Step Model: Assignment

Regional Model Assignment
• Equilibrium Assignment
• 24 Time Periods

Five Assignment Purposes
• SOV, LCV, HOV, SU, CMB

Three Paths
• SOV/LCV, HOV, Truck

Passenger Car Equivalency
Validation

Highway Link Volumes

VMT
Calibration Considerations

Data Availability
•
•
•
•
•


Vehicle Classification
Internal Origin-Destination
Trip Length Distribution
External Station Data
Commodity Data
Concurrent Regional Model Calibration
Regional Scope
Applications

Forecasting Truck Traffic
• Pavement Design
• Development Impacts

System Level Responses
• Modal Shift Impacts
• Value of Time/Benefit-Cost


Truck Impacts on Congestion
Grade Crossing Analysis
Recommendations for Further
Improvement

Commercial Vehicle Tracking Survey

National/Multi-State Commodity Flow Model

External Truck Intercept Survey

Vehicle Classification Data
Questions?