Using Aggregated Federal Data and Local Shipping Data to Model

Freight Alabama

Michael Anderson

Civil Engineering

The University of Alabama in Huntsville

Modeling Goals

• Develop a freight OD matrix

• Freight profile: scalable to the State, MPO or region to identify potential freight projects

National Freight Data

Local surveys

Regional, State and MPO Level Analysis

Integrated Freight Planning Framework

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FAF 2Data

FAZ

FAZ

FAZ

FAZ

FAZ

FAZ

FAZ

FAZ

FAZ

FAZ

TRANPLANDistribution

&VolumesInput to

ATIM

TRANPLAN/ ATIM

Interface

SystemPerformance

Measures

TRAN-PLAN

ATIM

Freight Analysis Zones

FAZ Forecast by Mode

Planning Factors – Value of Shipments, Personal Income, Population/Employment

Industry Sector Analysis

(Mode Dependent)

Passenger Car Data

Trip Generation

Trip Distribution Modal Split/ Assignment

Industry Surveys

Version 1.6

Statewide Application: Alabama

Freight Analysis Framework, Ver. 2

• 114 Zones

• 17 Ports of Entry

• 43 Commodities

• 7 Modes

• Disaggregation to county level Based on Personal Income and VOS

Pass Through Freight

Alabama IE, EI and EE Flows

Trucks/day ALDOT

Tons/year model Tons/day

Tons/truck

Pounds/ truck

I65 7,768 52,071,250 142,661 18.37 36,730

I59 4,758 47,408,170 129,885 27.30 54,601

I20 14,531 38,163,040 104,556 7.20 14,390

I85 6,070 42,259,400 115,779 19.07 38,149

I10E 6,334 13,234,480 36,259 5.72 11,450

I10W 9,979 22,101,760 60,553 6.07 12,136

I59W 8,875107,198,80

0 293,695 33.09 66,188

Initial Validation at State Boundary

Weighted average of tons per truck crossing Alabama’s borders is 15 tons. Differences in weight results from differences in commodities being shipped different directions.

Application of FAF2 – Statewide Model

• Internal to Zone 1• Internal to Zone 2• From Zone 1 to Zone 2• From Zone 2 to Zone 1 • From Zone 1 to locations outside

Alabama• From Zone 2 to locations outside

Alabama• From outside Alabama to Zone 1• From outside Alabama to Zone 2

• National Pass-Through

FAF2 - Alabama Statewide Model

R2 = 0.8159

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MPO Application: Mobile, AL

Mobile, AL: Convergence of two Interstates:I-10 running EWI-65 running NS

Mobile’s Freight Reality

• Class A Railroads in Mobile • Mouth of Alabama’s inland Waterways; 4500

miles of system via Tenn-Tom• 25 steam ship agencies• 4 foreign trade zones• 60 trucking companies• 4 bulk liquid terminals• 13 warehouses, 9 of which are US Customs

bonded• 16 shipbuilding or ship repair companies

Freight Modeling

• None!

• State of Alabama used “estimated” percentages for truck trips

• Truck trips were estimated percentage as a Non Home Based trips

• Trucks are not factored in the External to External trips, or Internal / External Trips

• No other mode than cars are modeled

Local Data

• What can industry input provide when developing a long-term freight plan?– Gain insight from companies to plan for

pattern shifts, network realignments, or current industry trends.

– Build relationships with business leaders so they become a vital source of planning intelligence.

Data Collection Tool

Key Data Points1. Business description2. Number of employees3. Mode of shipments4. Number of deliveries

received by mode weekly5. Number of shipments by

mode generated weekly6. Origins of inbound

deliveries (at least compass direction)

7. Destinations of outbound shipments (at least compass direction)

8. Size of shipment by mode (Full load, Less than full load)

9. Weight of shipments in pounds by mode (average/normal)

10. Size of facility in square feet (under roof)

11. Expansion plans for forecast period (5 years)

12. Value of Goods (dollars)13. Actual annual volume of

goods for prior year (should approximate Q5+Q6 x 52)

14. Forecasted annual volume of goods for next year

15. Transportation problems at their location

16. Transportation problems in the region

Conclusions to Local Data Collection

The information gathered through this process, along with information on commodity flows from around the country, allowed the MPO to produce an intelligent estimate of freight movement within the study area and resulted in a validated transportation model.

Trip Purposes

Mobile Freight Assignment

• Combination of FAF2 data and Regional Freight Profile

• Freight OD Matrix Entered as Preload

Overall Conclusions

• New Ability to Model Truck Trips– Scalable : Regional, Statewide, Local

• Use of FAF2 forecasts Local Freight Data

• Analyze projects considering freight impacts

Questions?

Michael Anderson

256-824-5028

mikea@cee.uah.edu

Office for Freight, Logistics & Transportation