F R E I G H T T R A V E L D E M A N D M O D E L I N G
C I V L 7 9 0 9 / 8 9 8 9
D E P A R T M E N T O F C I V I L E N G I N E E R I N G
U N I V E R S I T Y O F M E M P H I S
1
Lecture-10: Freight Mode Choice
11/14/2014
Outline2
1. Objective of Mode Choice
2. A simple Example
3. Discrete Choice Framework
4. The Random Utility Model
5. Binary Choice Models
6. Freight Mode Choice Models
7. Case Studies
Basics of Mode Choice (1)3
β’ Mode choice determines the number of trips between zones that are made by different modes (such as truck, rail, water, and air).
truck
rail
Basics of Mode Choice (2)4
1 2 3
1 a11 a12 a13
2 a21 a22 a23
3 a31 a32 a33
Trip Distribution
(Number of trips from origin->destination)
Mode Choice
(Number of trips from origin->destination
by mode)
1 2 3
1 π11π1 π12
π1 π13π1
2 π21π1 π22
π1 π23π1
3 π31π1 π32
π1 π33π1
1 2 3
1 π11π2 π12
π2 π13π2
2 π21π2 π22
π2 π23π2
3 π31π2 π32
π2 π33π21 2 3
1 π11π3 π12
π3 π13π3
2 π21π3 π22
π3 π23π3
3 π31π3 π32
π3 π33π3
πππ π1
πππ π2
πππ π3
π
π
ππ
πππ ππ = 1
Where,
r: origin
s: destination
mi: mode I
p: probability
Basics of Mode Choice (3)5
How to estimate the probability of choosing a mode
(πππ ππ)
Typically done by random utility maximization
Each mode is a choice given to the commodities to be carried from origin to destination
The attractiveness of each mode can be represented as an utility function
Derived from discrete choice theories
Letβs consider a simple example to get a glimpse of choice models
A Simple Example
6
Mobile Phone Choice
Education
Low(k=1) Medium(k=2) High(k=3)
Smartphone(i=1) 10 100 90 200
Feature phone (i=2) 140 200 60 400
150 300 150 600
25% 50% 25%
β’ Sample size: N = 600 (Random)
β’ Mobile phone choice (Smartphone, feature phone)
β’ Education (Low, Medium, high)
Example: Marginal and Joint Probabilities
β’ (Marginal) probability of choosing smartphone: P(i = 1)
π π = 1 =200
600=
1
3
β’ (Joint) probability of choosing smartphone and having medium level education: P(i = 1, k =2)
π π = 1, π = 2 =100
600=
1
6
π=1
2
π=1
3
π π, π = 1
Joint and Marginal Probabilities
Mobile Phone ChoiceEducation Mobile Phone
Choice MarginalLow(k=1) Medium(k=2) High(k=3)
Smartphone(i=1)10
600=
1
60
10
600=
1
60
90
600=
3
20
200
600=
1
3
Feature phone (i=2)140
600=
7
30
200
600=
1
3
60
600=
1
10
400
600=
2
3
Educational Marginal
150
600=
1
4
300
600=
1
2
150
600=
1
4
Example: Conditional Probability P(i|k)
β’ Bayesβ Theorem: P(i,k) = P(i) . P(k | i)
= P(k) . P(i | k)
Independence
P(i,k)=P(i)P(k)
P(i | k)=P(i)
P(k | i)=P(k)
π π =
π
π π, π
π π =
π
π π, π
π π|π =P(i,k)
P(i), π(π) β 0
π π|π =P(i,k)
P(k), π(π) β 0
Example: Modeling P(i | k)
β’ Behavioral Model- Probability (Mobile Phone Choice | Education) = P(i | k)
- Explains choice of mobile phone type given education level
β’ Unknown parameters
P(i =1| k=1) = π1
P(i =1| k=2) = π2
P(i =1| k=3) = π3
Example: Model Estimation
β’ Estimates of the unknown parameters:
π1 = π π = 1 π = 1 = π(π = 1, π = 1)
π(πΎ = 1)=
160
14
=1
15= 0.067
π2 = π π = 1 π = 2 = π(π = 1, π = 2)
π(πΎ = 2)=
16
12
=1
3= 0.333
π3 = π π = 1 π = 3 = π(π = 1, π = 3)
π(πΎ = 3)=
320
14
=3
5= 0.6
Sampling Distribution
β’ Estimates are not point values, but are distributed
β’ Hypothetical distribution around the true meanfr
equen
cy
Sampling
distribution
π2 π2
Standard Errors
β’ Compute the standard error of the estimates
β’ Since outcomes are Bernoulli random variables
π 1 = π1. (1 β π1)
π1=
1/15. (1 β 1/15)
150= 0.020
π 2 = π2. (1 β π2)
π2=
1/3. (1 β 1/3)
300= 0.027
π 3 = π3. (1 β π3)
π3=
3/5. (1 β 3/5)
150= 0.040
Standard errors
Standard Error of Forecast
β’ Predicted smartphone share:
π π = 1 =
π=1
3
ππ . π π
β’ The estimated standard of the predicted smartphone share is
π π(π=1) =
π=1
3
π π 2π π2 β 0.0175 = 1.75%
Discrete Choice Framework
β’ Decision-Maker
- Individual (person/household)
- Socio-economic characteristics (e.g. age, gender, education, income)
β’ Alternatives
- Decision-maker n selects one and only one alternative from a choice set
Cn = {1,2,β¦,i,β¦,Jn} with Jn alternatives
β’ Attributes of alternatives (e.g. price, quality)
β’ Decision Rule
- Dominance, satisfaction, utility etc.
Mobile Phone Choice
β’ Decision Maker: An individual or a household
β’ Choice set: Alternative mobile phones
β’ Decision rule: Utility maximization
β’ Utility function: Function of attributes of the mobile phone, such as price and quality
Decision Maker Choice
Utility maximization
- Decision maker chooses the alternative that has the maximum utility (and falls within the set of considered alternatives)
If U(smartphone)>U(feature phone), choose smartphone
If U(smartphone)<U(feature phone), choose feature phone
U(smartphone) = ?
U(feature phone) = ?
Constructing the Utility Function
β’ U(smartphone)= U(pricesmartphone, qualitysmartphone, β¦)
U(feature phone)= U(pricefeaturephone, qualityfeaturephone, β¦)
β’ Assume linearity (in the parameters)
U(smartphone) = π½1 Γ (pricesmartphone) + π½2 Γ(qualitysmartphone) + β¦
β’ Parameters represent tastes, which may bary across people
- Include socio-economic characteristics )e.g. age, gender, income)
- U(smartphone) = π½1 Γ (pricesmartphone/income) + β¦
+ π½2 Γ (qualitysmartphone)
Decision Maker Choice
If U(smartphone) - U(feature phone) > 0 , Probability(smartphone) = 1
If U(smartphone) - U(feature phone) < 0 , Probability(smartphone) = 0
1
00
P(smartphone)
U(smartphone) - U(feature phone)
Probabilistic Choice
β’ Random Utility Model (RUM)
Ui = V(attributes of I; parameters) + Ιi
β’ What is in the Ι ? Analystsβ imperfect knowledge
- Unobserved attributes
- Unobserved taste variations
- Measurement errors
- Use of proxy variables
β’ U(smartphone) = π½1 Γ (pricesmartphone) + π½2 Γ(qualitysmartphone) + β¦ +
Ιsmartphone= V(smartphone) + Ιsmartphone
3. The Random Utility Model
β’ Decision rule: Utility maximization
- Individual n selects the alternative i with the highest utility Uin
among those in the choice set Cn
β’ Utility: Uin = Vin + Ιin
β’ Vin: Systematic utility expressed as a function of k observable variables,
e.g. π π½ππ₯πππ = π½β²π₯ππ
β’ Ιin : Random utility component
The Random Utility Model (Cont.)
β’ Choice Probability
P(i|Cn) = P(Uin β₯ Ujn β j β Cn)
= P(Uin - Ujn β₯ 0,β j β Cn)
= P(Uin = maxj Ujn ,β j β Cn)
β’ For binary choice,
Pn(1) = P(U1n β₯ U2n)
= P(U1n β U2n β₯ 0)
Example with Attributes
π1 = π½1π1 β π½2π1 + π1π2 = π½1π2 β π½2π2 + π2
π½1, π½2 β₯ 0
Mobile Phone ChoiceAttributes
UtilityQuality (q) Price (p)
Smartphone(i=1) π1 π1 π1
Feature phone (i=2) π1 π2 π2
Example with Attributes(Cont.)
Ordinal utility
- Decision are based on utility differences
- Unique up to order preserving transformation
π1 = (π½1π1 β π½2π1 + π1 + πΌ)πβ
π2 = (π½1π2 β π½2π2 + π2 + πΌ)πβ
π½1, π½2, πβ β₯ 0
Example with Attributes(Cont.)
π1 =π½1
π½2π1 β π1 + π1
π2 =π½1
π½2π2 β π2 + π2
π½ =π½1
π½2= "π£πππ’π ππ ππ’ππππ‘π¦"
π1 =π½1
π½2π1 β π1
π2 =π½1
π½2π2 β π2
π1 β π2 = βπ½1
π½2π2 β π1 β π1 β π2 + π1 β π2
Example with Attributes(Cont.)
β’ Attributes: Describing the alternative
- Generic vs. Alternative Specific
β’ Examples: Price per call, monthly fee
- Quantitative vs. Qualitative
β’ Examples: Quality
β’ Characteristics: Describing the decision-maker
- Socio-economic
Random Terms
β’ Distribution of epsilons (Ξ΅)
β’ Variance/covariance structure
- Correlations between alternatives
- Multidimensional decision (e.g. mobile phone type and service provider)
β’ Typical models
- Probit (normal error terms)
- Logit (i.i.d. βExtreme Valueβ error terms)
4.Binary Choice Models
Choice Set Cn={1,2} β n
Pn(1) = P(1| Cn)=P( U1n β₯ U2n)
= P(V1n + π1n β₯ V2n + π2n)
= P(V1n βV2n β₯ π2n - π1n)
= P(V1n βV2n β₯ πn)
= P(Vn β₯ πn)= Fπ(Vn)
Binary Probit Model
βProbitβ name comes from Probability Unit
π1π ~ π(0, π12)
π2π ~ π(0, π22)
ππ ~ π(0, π2) where π2 =π12+π2
2 β 2π12
π π =1
π 2ππβ
12(
ππ)2
ππ 1 = πΉπ ππ = ββ
ππ 1
π 2ππβ
12(ππ
)2
ππ = Ξ¦(ππ
π)
Where Ξ¦(z) is the standardized cumulative normal distribution
Binary Logit Model
βLogitβ name comes from Logistic Probability Unit
π1π ~ Extreme value(0, π) πΉπ π1π =exp[-πβππ1π]
π2π ~ Extreme value(0, π) πΉπ π2π =exp[-πβππ2π]
ππ ~ Logistic (0, π) πΉπ ππ =1
1+πβπππ
ππ 1 = πΉπ ππ =1
1 + πβπππ
Logit vs Probit
β’ Probit
ππ 1 = Ξ¦ππ
π=
ββ
πππ 1
2ππβ
1
2π2
ππ = ββ
π1πβπ2ππ 1
2ππβ
1
2π2
ππ
β’ Logit
ππ 1 =1
1+πβπππ=
1
1+πβπ(π1πβπ2π) = πππ1π
πππ1π+πππ2π
β’ π1π = π½β²π₯1π, π2π = π½β²π₯2π, ππ = π½β²(π₯1π β π₯2π)
Why Logit?
β’ Probit does not have a closed form-the choice probability is an integral
β’ The logistic distribution is used because
β’ It approximates a normal distribution quite well
β’ It is analytically convenient
β’ Extreme Value distribution can be βjustifiedβ because of utility maximization
β’ Logit does have βfatterβ tails than a normal distribution
Summary
β’ A simple Example
β’ Discrete Choice Framework
β’ The Random Utility Model
β’ Binary Choice Models
β’ Probit ππ 1 = Ξ¦ ππ = ββ
ππ 1
2ππβ
1
2π2
ππ
(normalize Ο = 1)
β’ Logit ππ 1 =1
1+πβππ=
ππ1π
ππ1π+ππ2π
(normalize ΞΌ = 1)
β’ π1π = π½β²π₯1π, π2π = π½β²π₯2π, ππ = π½β²(π₯1π β π₯2π)
Additional Readings
β’ Ben-Akiva, M. and Lerman, S. (1985), Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, Cambridge MA, USA, Chapters 1-7.
Maximum Likelihood Estimation
Log likelihood function:πΏ π½ = πππΏβ π½ = π=1
π πππ π¦π ππ, π½ = π=1π πβπΆπ
πππ π πΆπ
Where π¦ππ = 1if n chose alternative I, 0 otherwise
Logit:
π π πΆπ =πππππ
πβπΆππ
ππππ, π = 1
πΏ π½ =
π=1
π
πβπΆπ
π¦ππ πππ β ππ
πβπΆπ
ππππ
Maximum Likelihood Estimation(cont.)
The maximum likelihood estimation problem:
π½ = arg πππ₯π½πΏ(π½1, π½2, β¦ , π½π)
FOC for linear in parameters Logit
πΏ π½ =
π=1
π
πβπΆπ
π¦ππ πππ β ππ
πβπΆπ
ππππ πππ = π=1
πΎ
π½πππππ
ππΏ(π½)
ππ½π=
π=1
π
πβπΆπ
π¦ππ π₯πππ β πβπΆπ
π₯πππππππ
πβπΆπππππ
= 0
π=1
π
πβπΆπ
π¦ππ β ππ π|π₯π, π½ π₯πππ = 0
Evaluation of Estimation Results
Signs and relative magnitudes of the coefficients For example, we expect the coefficient of price to be negative
Statistical tests
Goodness of fit measures
Statistical tests
Asymptotic t-test
- Used to test if a particular parameter is different from zero or from another known value
- Standard output statistic with most software
π»0: π½π = π½π0
π‘π = π½π β π½π
0
π π½π
- Compare to the critical value of the t distribution at a given level of significance
Statistical tests (cont.)
Likelihood ratio test- Tests nested hypotheses (restrictions in the model)
The statistic β2 πΏ 0 β πΏ π½
- Used to test the null hypothesis that all parameters are zero
- It is asymptotically π2 distributed with K (number of parameters) degrees of freedom
The statistic β2 πΏ π β πΏ π½
- Used to test the null hypothesis that all the parameters other than
the intercept are zero
- It is asymptotically π2 distributed with K-1 (number of restrictions) degrees of freedom
Goodness of Fit
π2 and π2 are analogous to π 2 and π 2 in least squares.
Likelihood ratio index π2: measures the fraction of an initial log likelihood that is explained by the model
π2 β‘ 1 βπΏ( π½)
πΏ(0)
π2 is monotonic in the number of parameters K
π2 must lie between 0 and 1.
Goodness of Fit
Adjusted π2 ( π2)
- Useful for comparison of models with different number of parameters (i.e., different degrees of freedom)
π2 β‘ 1 βπΏ π½ β πΎ
πΏ(0)
Extensions of Binary Choice Models44
Classical extensions
Multinomial Logit (MNL)
NL (Nested Logit)
CNL (Cross Nested Logit)
MMNL (Mixed Multinomial Logit)
Logit fixed effect / random effect
Advanced
Latent class
Bayesian
Hybrid
Maryland Freight Mode Choice Model45
Source: Mishra, S., Zhu, X, and Fucca, F. βAn integrated framework for Modeling freight mode and route choiceβ Report for Maryland State Highway Administration. [Download]
Growing awareness of freight system
Thrust at federal, state and local level
Marylandβs freight transportation is estimated
To grow about 105% by 2035
1.4 billion of total tons
4.98 trillion of $ value transfer (108% increase from 2006)
Sustainability of MD corridors to meet the future demand
Background
Why Freight Mode Choice?
Freight demand by mode varies by
Type of commodity
Value and size of commodity
Travel characteristics near distribution centers
Finer level geometric detail
Detailed Origin-Destination analysis within Maryland
Land use impact on freight flows
LOS identification and project selection
Objectives
Develop methods to forecast freight shipments By rail
By highway
Number of trucks
Time of day
Other
Multimodal
Other
Capable of responding to external changes Fuel price
New distribution centers
Tolling
Freight corridors
Mode Choice Factors
Develop methods to forecast freight shipments By rail
By highway
Number of trucks
Time of day
Other
Multimodal
Other
Capable of responding to external changes Fuel price
New distribution centers
Tolling
Freight corridors
Data
Available from Freight Analysis Framework (FAF)
Annual Macroscopic North American Freight Flow
Tons, Value, Distance, Commodity, Mode
Derive large scale long distance movements
Not available from FAF
Through trips (route)
Short distance internal trips
Cost (fuel price, time)
Just in time delivery
FAF Zones
131 FAF Zones123 nationwide8 international
3 MD FAF Zones Baltimore-MD Washington-MD Remainder-MD
Freight in Maryland
Within
MD
Leaving
MD
Arriving
in MD
Through
(Northeast-
Southeast)
Weight
(million of tons)135 84 91 52
Value (billion$) 92 113 169 177
Value/Weight
(Thousand $/ton)0.7 1.3 1.9 3.4
Northeast: CT, ME, MA, NH, NJ, NY, RI,VTSoutheast: FL, GA, NC, SC
External and Internal Trips By Mode
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Weight Value Weight Value Weight Value Weight Value
Within Maryland Leaving MD Arriving in MD Through (NE-SE)
Truck Rail Multimode Other
Commodities by Truck(From MD)
Lower Truck Percentage (<40%)
Medium Truck Percentage (41%-80%)
High Truck Percentage (>80%)
From MD
Commodities by Truck (To MD)
To MD
Lower Truck Percentage (<40%)
Medium Truck Percentage (41%-80%)
High Truck Percentage (>80%)
Commodities by Truck (Within MD)
Within MD
Lower Truck Percentage (<40%)
Medium Truck Percentage (41%-80%)
High Truck Percentage (>80%)
Proposed Model Structure
Within MD
Leaving MD
Arriving in MD
Com 1
Com 2
Com 3
4 models for different OD and Commodities
From To From To From To
1 Live animals fish 3 3 15 Coal 3 3 29 Printed prods 1 1
2 Cereal grains 3 3 16 Crude petroleum 3 3 30 Textiles leather 2 2
3 Other ag prods 3 3 17 Gasoline 3 3 31 Nonmetal min. prods 2 3
4 Animal feed 3 3 18 Fuel oils 3 3 32 Base metals 2 2
5 Meat seafood 3 3 19 Coal-n.e.c. 2 2 33 Articles-base metal 1 2
6 Milled grain prods 3 3 20 Basic chemicals 1 2 34 Machinery 2 2
7 Other foodstuffs 3 3 21 Pharmaceuticals 2 1 35 Electronics 2 2
8 Alcoholic beverages 3 3 22 Fertilizers 2 3 36 Motorized vehicles 2 1
9 Tobacco prods 3 3 23 Chemical prods 1 1 37 Transport equip 2 3
10 Building stone 3 3 24 Plastics rubber 2 1 38 Precision instruments 1 2
11 Natural sands 3 3 25 Logs 3 2 39 Furniture 3 2
12 Gravel 3 3 26 Wood prods 3 2 40 Misc. mfg. prods 2 2
13 Nonmetallic minerals 2 2 27 Newsprint paper 1 3 41 Waste scrap 3 3
14 Metallic ores 1 3 28 Paper articles 2 2 43 Mixed freight 2 2
Proposed Method
Aggregated analysis
Using land use as the factor
Logistic Regression Models
πππ is the probability of Truck Tonnage share
πππ is the Info of distribution centers,
highway/railway coverage,transportation/warehousing employment.
πππππ‘ πππ = ππππ½π + πππ
πππ
ππ.ππ€π.π
1 βππ.ππ€π.π
= ππππ½π + πππ
=π½0 + π½1π·ππ π‘ + π½2 π·πΆπ + π½3 π·πΆπ· + π½4 πΆππ£π + π½5 πΆππ£π· +π½6 πΈπππ + π½7 πΈπππ· β¦ + πππ
1 2 β¦ 123
β¦
β¦
48 π€48.1 π€48.2 π€48.123
49 π€49.1
50 π€50.1 π€50.123
β¦
Summation of all group 1 tonnage from MD
1 2 β¦ 123
β¦
β¦
48 π48.1 π48.2 π48.123
49 π49.1
50 π50.1 π50.123
β¦
Summation of all group 1 trucktonnage from MD
Proposed Model Structure
The share of truck ππ‘ =exp(π¦)
1+exp(π¦)
y = 0.431 β 0.002π1 + 2.463π2 β 0.164π3 + 0.414π4 β 0.024π5 + 0.286π6 β0.133π7
Parameter Estimates95% CI Lower
95% CI Upper
Wald Chi-Square
Sig.
(Intercept) X0 .431 -2.580 3.442 .079 .779
Highway distance X1 -.002 -.003 -.001 19.315 .000
# Origin zone truck center
X2 2.463 .417 4.508 5.569 .018
# Origin zone rail center
X3 -.164 -.272 -.055 8.766 .003
# Destination zone truck center
X4 .414 .108 .720 7.018 .008
# Destination zone rail center
X5 -.024 -.056 .007 2.265 .132
# Destination zone port center
X6 .286 -.075 .647 2.412 .120
# Destination zone Trans employment
(10K)X7 -.133 -.310 .044 2.160 .142
Example: From MD group1
Example: From MD group1
Parameter Estimate
s
(Intercept) X0 .431
Highway distance X1 -.002
# Origin zone truck center
X2 2.463
# Origin zone rail center X3 -.164
# Destination zone truck center
X4 .414
# Destination zone rail center
X5 -.024
# Destination zone port center
X6 .286
# Destination zone Trans employment (10K)
X7 -.133
β’ For this group of commodities, the total truck share from MD is less than 40%.
β’ The truck percentage share decrease with longer distance between the Origin and Destination zone.
β’ The number of truck-truck centers in MD influence the truck share dramatically.
β’ More number of rail centers in MD reduce the truck share.
β’ Truck share is high to the destination zone with more truck and port oriented centers and less rail centers, and less transportation/warehousing employment.
Example: From MD group1
The total group 1 commodity shipped from Baltimore (MD MSA) to Denver (CO CSA) ππ‘=62.3%
If there is one more port related distribution center in Baltimore
The truck share does not change.
If there is one more truck center in Baltimore ππ‘=95.1%
If there is one more rail center in Baltimore ππ‘=58.3%
Example: From MD group1
If the Destination zone is Jacksonville (FL MSA) Distance reduces from 1,591 m to 756m.
Employment reduces from 5.17 to 3.22 10K.
ππ‘=91.9%
With one more port-truck distribution center in Baltimore The truck share does not change.
If there is one more truck center in Baltimore ππ‘=99.3%
If there is one more rail center in Baltimore ππ‘=90.6%
Example: From MD group2
Parameter Estimates95% CI Lower
95% CI Upper
Wald Chi-
SquareSig.
(Intercept) X0 .689 -.542 1.920 1.204 .273
Highway distance X1 -.002 -.003 -.002 65.168 .000
# Destination zone rail center X2 -.022 -.044 .000 3.676 .055
Destination zone Principal arterialpercentage out of total highway and rail
mileageX3 3.660 .822 6.498 6.388 .011
# Destination zone Trans employment (10K) X4 .112 .013 .210 4.956 .026
For this group of commodities, the truck share from MD ranges from 40% to 80%.
The characteristics in Maryland do not impact the truck share.
The truck share only depends on the destination zone.
The truck is preferred to the zones closer to Maryland, with less rail distribution centers, higher Principal Arterial roadway and more transportation related employments.
Example: To MD group1
Parameter Estimat
es95% CI Lower
95% CI Upper
Wald Chi-Square
Sig.
(Intercept) X0 2.720 2.019 3.421 57.850 0.000
Highway distance X1 -0.001 -0.001 0.000 3.981 0.046
# Origin zone port related distribution center
X2 -0.158 -0.373 0.058 2.060 0.151
Destination zone rail center percentage X3 -2.020 -3.246 -0.794 10.431 0.001
# Origin zone Trans employment (10K) X4 0.040 -0.023 0.102 1.565 0.211
β’ The percentage of rail oriented distribution centers in Maryland is negative related with the truck share.
β’ The truck share also depends on the origin zone # port related centers, transportation employments.
β’ The truck is preferred from the zones closer to Maryland, with less port distribution centers, and more transportation related employments.
Example: To MD group2
β’ The characteristics in Maryland do not impact the truck share.
β’ The truck is preferred from the zones closer to Maryland, with more transportation related employments.
Parameter Estimates95% CI Lower
95% CI Upper
Wald Chi-Square
Sig.
(Intercept) X0 3.055 1.351 4.760 12.340 .000
Highway distance X1 -.002 -.003 -.002 54.749 .000
Origin zone percentage of rail miles out of total highway and rail mileage
X2 -3.576 -7.274 .123 3.590 .058
# Origin zone Trans employment (10K) X3 .074 .000 .147 3.882 .049
Choice Model for Rail
Parameter B
95% Wald Confidence
Interval
Hypothesis
Test
Lower Upper
Wald Chi-
Square
Group1
Commodity
from MD
(Intercept) 5.525 2.933 8.117 17.46
Truck_dist -0.001 -0.002 0 6.533
D_Port 0.29 -0.002 0.582 3.783
D_PAHwy_P -12.539 -17.422 -7.655 25.324
Group2
Commodity
from MD
(Intercept) 3.822 -0.862 8.506 2.557
Truck_dist -0.002 -0.003 -0.001 23.284
D_truck -0.228 -0.381 -0.075 8.536
D_PAHwy_P -14.252 -20.424 -8.08 20.486
Group1
Commodity to
MD
(Intercept) -2.339 -4.357 -0.32 5.158
Truck_dist -0.001 -0.002 0 6.233
O_truck -0.276 -0.461 -0.091 8.558
O_rail 0.155 0.101 0.209 31.586
D_TC_P -6.958 -12.129 -1.787 6.954
Group2
Commodity to
MD
(Intercept) 7.195 4.799 9.592 34.62
Truck_dist 0 -0.001 -6.50E-05 5.541
O_truck 0.127 0.008 0.246 4.349
O_rail 0.044 0.019 0.069 11.756
D_TC_P -2.173 -3.488 -0.858 10.495
D_RC_P -5.759 -8.147 -3.372 22.361
O_PAHwy_P -8.946 -12.704 -5.188 21.774
Sensitivity Analysis Results
Parameter 48 49 50
Group 1 from MD
# Origin zone truck center X2 1.2314 1.209 1.0761
# Origin zone rail center X3 0.9763 0.9783 0.9904
# Destination zone truck center X4 1.0545 1.0498 1.0213
# Destination zone rail center X5 0.9966 0.9969 0.9986
# Destination zone port center X6 1.0384 1.0351 1.0152
# Destination zone Trans
employment (10K)X7 0.9809 0.9825 0.9923
Group 2 from MD
# Destination zone rail center X2 0.9930 0.9931 0.9928
Destination zone principal
arterial percentage out of total
highway and rail mileage (1%)
X3 1.0115 1.0114 1.0120
# Destination zone Trans
employment (10K)X4 1.0352 1.0349 1.0366
Group 1 to MD
# Origin zone port related
distribution centerX2 0.9474 0.9713 0.9413
Destination zone rail center
percentage (1%)X3 0.9934 0.9964 0.9926
# Origin zone Trans employment
(10K)X4 1.0131 1.0069 1.0147
Group 2 to MD
Origin zone percentage of rail
miles out of total highway and
rail mileage (1%)
X2 0.9883 0.9883 0.9878
# Origin zone Trans employment
(10K)X3 1.0242 1.0240 1.0252
Summary
For Group 1 commodities, number of truck and rail centers will influence the percentage of tonnage carried by truck.
For Group 2 commodities, the percentage of truck tonnage only depends on the characteristics of the opposite zones.
The distance is a dominant variables related to truck share.
The principal arterial highway and rail coverage in the opposite zones are related to truck share for group 2, not group 1.
Number of transportation/warehousing employments in the opposite zones is significant.
Variables such as highway and rail coverage in MD and employment in MD is not related.
Potential Applications
Forecast of Future Freight Demand
Expansion of the Port of Baltimore
Expansion of Panama Canal and Northwest passage
Prevent Infrastructure Bottlenecks
Intermodal Facilities
Truck Distribution Centers
Economic Analysis
Project selection
Dollars lost by not providing infrastructure
Chicago Freight Mode Choice Model78
Amir Samimia*, Kazuya Kawamurab and Abolfazl Mohammadian. (2011). A behavioral analysis of freight mode choice decisions. Transportation Planning and Technology, Vol. 34, No. 8, p. 857-869.
Data Collection79
Collected 881 domestic shipments in the United States.
A total of 4544 business establishments opened the recruiting email, of which 316 firms participated
A 7% response rate, which is a reasonable rate in such surveys.
Basic information about the following is obtained each establishment
five recent shipments, namely origin, destination, transportation mode, type, value, weight, and volume of the commodity
cost and time of the entire shipping process
Modeling Approach81
Used random utility maximization approach
Binary probit and logit
Goodness of fit
AIC
Log-likelihood
Rho-square
Sample validation for both modes
Predicted versus modeled
London Freight Mode Choice Model84
Kriangkrai Arunotayanun , John W. Polak. (2011). Taste heterogeneity and market segmentation in freight shippersβ mode choice behaviour. Transportation Research Part E, 47, pp. 138-148.
Sweden Freight Mode Choice Model88
Gerard de Jong , Moshe Ben-Akiva. (2007). A micro-simulation model of shipment size and transport chain choice, Transportation Research Part B 41, pp.950β965.
Model Summary89
Multinomial logit model for combined shipment size and mode choice
Weight choices
Up to 3500 kg
3501β15,000 kg
15,001β30,000 kg
30,001β100,000 kg
Above 100,000 kg
Nested and mixed models were not significant