Freight Demand Modeling Using Econometric ModelsSept 14, 2010

Econometric Models….• Use regression based approaches to

estimate demand• Typical statistical techniques used are - Ordinary Least Squares (OLS) - Panel Models - Others (Two Stages, etc)• Use historical data

Are particularly useful when…• Long period of historical data exists• Socio-economic factors are important, for

example at a major interstate with significant proportion of thru-traffic

• No significant network changes are expected (e.g. competing route construction)

Advantages of Econometric Models• Easy to develop and estimate

• Simulation of economic variables, toll and other scenarios can be developed in a statistically rigorous way

• Relatively inexpensive to update and recalibrate

Disadvantages of Econometric Models• Theoretically relevant independent

variables can be highly correlated (multi-collinearity)

• Assumption that historical data is a good predictor of future trends (tenuous when structural change might be taking place)

Typical Model Structure• ….generally consists of…

Truck Transactions / AADT = f (tolls, diesel prices, economic factors (e.g. employment, industrial production, inventory accumulation, inventory sales ratios), seasonal dummies, special one-off events)

AN EXAMPLE APPLICATION

Michigan / Canada Border Crossing

Michigan / Canada Border Crossing• Freight Corridor model of Ambassador and Blue

Water Bridges

Independent Demand Drivers• Diesel Price• Foreign Exchange Rate • US Industrial Production• US Inventory Sales Ratios• US Light Vehicle Sales • Seasonal Dummies• Blue Water Deck Replacement

The Challenges…• High Correlation between Foreign Exchange Rate and Diesel Price• Solution: Principal Component Analysis

1995

.319

96.219

97.119

97.419

98.319

99.220

00.120

00.420

01.320

02.220

03.120

03.420

04.320

05.220

06.120

06.420

07.320

08.220

09.1

50

100

150

200

250

300

350

400

450

500

0.5

0.6

0.7

0.8

0.9

1

1.1

US Diesel Price (cents)

US $ Per Can $Deisel Price

US $ Excange Rate

Correlation =0.9

Principal Component Analysis

• Used to address high correlation of independent variables (or multi-collinearity)

• Technique captures “underlying” trend of the data (by computing a weighted average)

• Derived components are uncorrelated to each other

-2

-1

0

1

2

3

4

5

0.5

0.6

0.7

0.8

0.9

1

1.1

US $ Per Can $

Diesel Price / Foreign Ex-change Principal component

Why do we use Principal Component Techniques here?• Reduce multi-collinearity between Diesel

Price and the exchange rate

• Incorporate more information: Principal component is derived for Inventory Sales Ratio (wholesale, retail, manufacturing, total business)

Model Results• Sample 1995Q3 – 2007 Q4

Variable Coefficient Std. Error t-Statistic Prob.

C 8.22 0.27 30.66 0.00Principal Component (Diesel +

Foreign Exchange) -0.05 0.01 -9.56 0.00Principal Component Inventory

Sales Ratio -0.03 0.00 -7.67 0.00LOG(US Light Vehicle Sales) 0.22 0.09 2.47 0.02LOG(Industrial Production) 1.12 0.07 16.04 0.00

Q1 0.01 0.01 1.58 0.12Q2 0.06 0.01 6.89 0.00Q3 -0.02 0.01 -2.33 0.02

Dummy Sept 11 -0.03 0.01 -2.10 0.04Blue Water Span Replacement 0.03 0.01 2.63 0.01

R-squared 0.979 Mean dependent var 13.973Adjusted R-squared 0.974 S.D. dependent var 0.134S.E. of regression 0.022 Akaike info criterion -4.659

Dependent Variable: LOG(TOTAL_TRAFFIC)Method: Least Squares

Do the Models Forecast the Recession?

• Models Backcast very well with MAPE of 2.3% for Inventory based models

• For the naïve GDP model MAPE of 7.5%

1995Q

3

1996

Q2

1997Q

1

1997

Q4

1998

Q3

1999

Q2

2000

Q1

2000

Q4

2001

Q3

2002Q

2

2003Q

1

2003

Q4

2004Q

3

2005

Q2

2006

Q1

2006

Q4

2007

Q3

2008

Q2

2009

Q1 700,000

800,000

900,000

1,000,000

1,100,000

1,200,000

1,300,000

1,400,000

1,500,000

Total Traffic

Inventory Model

Naïve GDP Model

AN EXAMPLE APPLICATION

New York State Thru-way

The New York Thru-way Model• Models estimated for different sections of

the New York State Thruway

• In particular models focus on sections near Buffalo and Lake Eerie, providing connections across the border into Canada

Panel Models• Panel Models are estimated for different

sections of the New York State Thruway• Panel models are jointly estimated for a

common set of coefficients• Fixed effect methods control for section

specific (unobserved) characteristics

Model Specification• Inventory Sales Ratio Principal Component

(includes retail, manufacturing, wholesale, total)

• Industrial Production• US Diesel Prices• Monthly dummies and other event dummies

Model Backcasts

Jan-0

5

May-05

Sep-05

Jan-0

6

May-06

Sep-06

Jan-0

7

May-07

Sep-07

Jan-08

May-08

Sep-08

Jan-0

9

May-09Se

p-09 100,000

120,000

140,000

160,000

180,000

200,000

220,000

240,000

260,000

280,000

300,000

Model Backcast

Buffalo Backcast

Buffalo Traffic

Dec-05

Mar-06

Jun-0

6Se

p-06

Dec-06

Mar-07

Jun-0

7Se

p-07

Dec-07

Mar-08

Jun-0

8Se

p-08

Dec-08

Mar-09

Jun-0

9Se

p-09 190,000

195,000

200,000

205,000

210,000

215,000

220,000

225,000

1 Year Moving Average

Buffalo BackcastBuffalo Traffic

Conclusions• Inventory based models work well and value

compared naïve models based on a broad based macro-economic indicator

• Principal component techniques can be successfully used to address multicollinarity issues

• When well calibrated, econometric models can successfully capture even unprecendent declines in activity