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Page 1: A behavioral analysis of freight mode choice decisionssharif.edu/~asamimi/site_files/Journal Papers/(2011) A Behavioral... · A behavioral analysis of freight mode choice ... behavioral

A behavioral analysis of freight mode choice decisions

Amir Samimia*, Kazuya Kawamurab and Abolfazl Mohammadianc

aDepartment of Civil Engineering, Sharif University of Technology,Azadi Avenue, Tehran 11365-8639, Iran; bCollege of Urban Planning and Public Affairs,University of Illinois at Chicago, 412 S. Peoria Street, Chicago, IL 60607-7064, USA;

cDepartment of Civil and Materials Engineering, University of Illinois at Chicago, 842 W. TaylorStreet, Chicago, IL 60607-7023, USA

(Received 3 November 2009; accepted 1 June 2011)

This paper develops a behavioral analysis of freight mode choice decisions thatcould provide a basis for an acceptable analytical tool for policy assessment. Thepaper specifically examines the way that truck and rail compete for commoditymovement in the US. Two binary mode choice models are introduced inwhich some shipment-specific variables (e.g. distance, weight and value) andmode-specific variables (e.g. haul time and cost) are found to be determinants.The specifications of the non-selected choice are imputed in a machine learningmodule. Shipping cost is found to be a central factor for rail shipments, whileroad shipments are found to be more sensitive to haul time. Sensitivity of modechoice decisions is further analyzed under different fuel price fluctuationscenarios. A low level of mode choice sensitivity is found with respect to fuelprice, such that even a 50% increase in fuel cost does not cause a significant modalshift between truck and rail.

Keywords: freight mode choice; truck and rail competition; fuel cost fluctuation;machine learning

Introduction

An efficient and reliable freight transportation system has substantial effects on

growth and sustainability of the national economy. This is because in such a system,

transportation cost will be reduced in the production process in several ways such as

decreasing inventory, labor, operating and maintenance costs (ICF Consulting and

HLB Decision-Economics 2002). Also, reducing the burden of freight traffic on the

transportation network will bring significant benefits to society through savings

in travel time, fuel consumption, pollution and diminishing other negative

consequences of an overburdened transportation system. According to the US

Commodity Flow Survey (CFS), the total value of transported commodities

increased around 30% between 1993 and 2002, and by the year 2035 the 2002

number is expected to double (US Department of Transportation 2006). The

enormous increase in freight traffic flow will require appropriate actions to address

the negative impacts of freight transportation activities.In the US, trucking is the most prolific among the freight transportation modes,

accounting for 69% of the total tonnage nationally in 2007 (US Department of

*Corresponding author. Email: [email protected]

Transportation Planning and Technology

Vol. 34, No. 8, December 2011, 857�869

ISSN 0308-1060 print/ISSN 1029-0354 online

# 2011 Taylor & Francis

http://dx.doi.org/10.1080/03081060.2011.600092

http://www.tandfonline.com

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Transportation 2010). However, there is a consequence of the over-reliance on

trucking. Forkenbrock (2001) compared the externalities of rail and truck shipments,

revealing the former to be considerably less. According to his study, the external costs

of trucking were found to be over three times that of rail. While determining the

socially optimal balance between different freight modes will require major research

efforts (and is not the purpose of this paper), it is obvious that furthering the

understanding of modal selection behavior and having a more reliable analytical toolwill facilitate the development of broad strategies.

Over the years, there have been some notable efforts to develop such a tool.

However, freight shipment decisions have been changing rapidly during the past

three decades in response to the need for leaner, more efficient supply chain systems

that was brought on by the globalization of manufacturing process. The complexity

of today’s logistics decision-making process presents a serious challenge for freight

demand modelers to provide reliable analytical tools for both policy-makers and

practitioners. This problem can be mainly attributed to the lack of appropriate

disaggregate freight data, which prevents researchers from developing realistic

behavioral models. Meanwhile, the use of operations research (OR) techniques in

the intermodal freight transport arena is still limited (Macharis and Bontekoning

2004). This is primarily due to the complexity of such systems in which many

decision-makers are involved in a multi-commodity and multi-modal network with

several constraints. Researchers are introducing new OR-based approaches by

simplifying the problem and applying heuristic methodologies to solve multi-objective (e.g. cost, speed, reliability and risk) problems of this type (Min and

Glaister 1991). Furthermore, some disaggregate data collection efforts are under way

to introduce robust behavioral freight models (Roorda et al. 2010). It is worthwhile

to note that disaggregate data are being collected regularly by the US Department of

Transportation (2006) in their CFS. Nevertheless, this information is only available in

an aggregate format to respect the privacy of the business establishment.

Mode choice is one of the most critical parts of any freight demand modeling

framework. However, the literature on this issue is surprisingly modest mainly due to

the absence of suitable data. A direct comparison of shipment costs was the primary

method in the most early freight mode choice models (Cunningham 1982). However,

reliability, flexibility, safety and some other non-cost factors entered the analysis

when the random utility models emerged (Norojono and Young 2003). On the other

hand, implementation of supply chain management concepts along with the

deregulation of freight industries drastically affected the shipping behaviors of

companies (Rodrigue 2006). New supply chain concepts (e.g. just-in-time) were

adopted by many companies, which subsequently influenced shipping preferences(Hensher and Figliozzi 2007) and required fundamental revision in the models.

Arunotayanun and Polak (2007) found transport cost, delivery time, quality and

flexibility of service as the significant determinants of freight mode choice in

multinomial and mixed multinomial logit specifications. Although their analysis

included four commodity types, some critical information on each shipment such as

size, value and distance were missing. Evers et al. (1996) also asked shippers in

Minnesota to rate truck, rail and intermodal modes of freight transport on 17

characteristics. Six essential factors in freight modal selection were then introduced,

among which reliability and availability of each mode were ranked the highest. This

finding is in line with several other studies that found haul time and reliability to be

858 A. Samimi et al.

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more important than the cost to the shippers (McGinnis 1979). Evers et al. (1996)

tried to capture the role of shippers’ perceptions of the modes and the past

experiences in the analysis as well. A number of studies accounted for commodity

and industry heterogeneity in freight modal selection models; however, these models

are still at an early stage (Nam 1997, Arunotayanun and Polak 2007). For instance,

non-perishable food, textiles, leather and electronics were the only commodity types

that were considered by Arunotayanun and Polak (2007). Although the small

number of categories imposes some limitations on the study’s conclusions, such

restrictions are unavoidable in many freight studies. Nevertheless, based on the

review of those studies, the dominant factors impacting on freight mode choice in the

literature can be summarized as: accessibility, reliability, cost, time, flexibility and

past experience with each mode.

This paper introduces binary logit and probit models that explain how truck and

rail are chosen as the preferred mode by shippers, third party logistics providers

(3PLs) or receivers. These models specifically look into transportation cost, distance,

weight and value of commodities, and access to truck�rail intermodal facilities.

A specific sensitivity analysis is also performed to show how freight mode choice

changes with fluctuations in fuel cost. Modeling results and data used for calibration

are presented in the next section, followed by an in-depth analysis of the findings.

Finally, conclusions and recommendations are provided.

Data and model

Freight mode-choice studies are performed traditionally using OR techniques, and

more recently by utility maximization theory. The latter is becoming more common,

as the logistics decision-making process has become extremely sophisticated and

factors other than cost (e.g. speed, reliability and risk) are playing a significant role

(Arunotayanun and Polak 2007). Complex behaviors of decision-makers in the

current freight transport market, along with the extent of the freight transport

systems, necessitate several simplifying assumptions in OR-based cost-minimization

approaches. Hence, a random utility maximization approach was chosen in this

study. However, any disaggregated data on freight activities are extremely difficult to

obtain due to the scarcity of the surveys that collect such data and the concern for

violating the confidentiality of the businesses that participated in the survey. Thus, it

is not surprising that there is no disaggregate freight data at the national level that

are publicly available in the US.

Therefore, our effort to develop a freight mode choice model had to begin with a

data collection effort. An online survey was conducted at the University of Illinois,

Chicago (UIC) in April and May 2009, providing information on 881 domestic

shipments in the United States (Samimi et al. 2010a). 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

each establishment along with data on five recent shipments, namely origin,

destination, transportation mode, type, value, weight, and volume of the commodity,

cost and time of the entire shipping process, were obtained (Samimi et al. 2010a, b).

Some essential information about each establishment was also collected: square

footage, number of employees, industry type, location, warehousing situation and

Transportation Planning and Technology 859

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potential use of each freight transportation mode. Table 1 shows a comparison

between value and weight of the surveyed commodities and the 2002 CFS data.

Many shippers are reluctant to participate in surveys that enquire about their

shipping decisions, which results in low response rates that can diminish the

credibility of or even invalidate the study results, if not appropriately addressed.

A comprehensive analysis of non-random selection bias was performed in a separate

study (Samimi et al. 2010a) to test whether size, location and industry type of the

firms has affected the probability of participation. The two-stage Heckman

correction (Heckman 1990) method was discussed, revealing no significant effect

of establishment size and a very minor and negligible effect of location and industry

type on the probability of participation. Brief statistics of variables that are used in

the final mode choice models are presented in Table 2.

A proper choice model is sensitive to attributes of both decision-maker and

choice alternatives. While characteristics of the decision-maker (e.g. number of

employees) do not change across alternatives, the attributes of choice alternatives

vary significantly from one alternative to the other (e.g. shipping time) and are

typically collected only for the observed choice. One of the critical challenges in

modeling freight modal selection is to obtain information on non-selected choices. In

our case, shipping cost and time for using either truck or rail was obtained for each

shipment in the survey. Using those data, the specifications of the non-selected

choice were imputed in a machine learning module.

Artificial neural networks (ANNs) are a class of learning algorithms that develop

learning rules based on data and construct a so-called ‘black box’ that can be used to

generate desired outputs that correspond to a new set of inputs. Although ANNs

generate very complicated rules that usually produce high levels of fit, the underlying

rules by which the output is generated are not revealed. Machine learning methods

have been implemented in the field of transportation planning in the past (Al-Deek

2001, Mohammadian and Miller 2003), and a more complete discussion on the topic

can be found in the literature (Parks et al. 1998, Principe et al. 2000).

For this study, two artificial neural networks were constructed with two hidden

layers and were trained using NeuroSolution v5.07 (Neuro Dimension Corporate

2009). The input data were divided into three parts: 60% of the data was used for

training the networks; 15% for cross validation; and the remaining 25% was left aside

as the test set to evaluate the quality of the trained network. The first network was

Table 1. Value and weight share of each mode.

Dollar value Weight Shipments

Mode CFS (%)a UIC (%)b CFS (%) UIC (%) UIC (%)

Truck 68 67 60 49 69

Rail and rail intermodalc 3 4 10 12 5

Other 1 8 4 8 5

aCommodity Flow Survey (2002) data do not include imports and exports that pass through the UnitedStates from a foreign origin to a foreign destination by any mode.bUIC (University of Illinois at Chicago) National Freight Survey.cIntermodal includes US Postal Service and courier shipments and all intermodal combinations, except airand truck.

860 A. Samimi et al.

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trained using data for the rail shipments to impute the unobserved shipping times

and costs for road shipments, while the other network used truck shipments for

training to estimate the aforementioned information for rail shipments.

The most common framework used for choice behavior analysis in recent years

has been the discrete choice modeling approach. Various forms of discreet choice

models are proposed in the literature depending on underlying assumptions

concerning the distribution of the unobserved utility. Two widely used forms of

discrete choice models are logit and probit models. While the logit model assumes

independent and identically distributed (IID) error terms in the utility function, the

probit model assumes a normal distribution for the error terms (Train 2003). Limdep

econometrics software (Greene 2002) was used to import explanatory variables such

as: shipping mode, time of each mode, cost of each mode, distance, commodity type,

weight, volume, value, access to truck�rail intermodal facilities, potential use of rail,

etc. The forward selection method was used for variable selection, and numerous

forms and combinations of variables were tested for the most appropriate fit. Table 3

Table 2. Variables used in the analysis.

Variable Definition Mean

Standard

deviation

Mode 1: rail or any combination of that with other

modes/0: truck

0.089 0.285

Distance Suggested distance between origin and

destination by Google Map (miles)

1077.940 2221.100

Weight Weight of the shipment (lbs) 22,901.100 25,275.100

Value Value of the shipment (USD) 48,101.389 130,150.251

Truck-cost Shipping cost by truck (USD) 1331.760 4093.390

Rail-cost Shipping cost by rail (USD) 2016.880 1128.160

Truck-time Shipping time by truck (days) 2.012 1.357

Rail-time Shipping time by rail (days) 7.281 6.662

Truck-cost-index �Ln (TRUCK-COST/(TRUCK-

TIME�VALUE))

�3.542 1.521

Rail-cost-index �Ln (RAIL-COST/(RAIL-

TIME�VALUE))

�3.705 1.940

Same-decision 1: if the same mode was preferred TWO years

ago for a similar shipment/0: otherwise

0.934 0.248

Access 0: firm has easy access to truck rail intermodal

facilities/1: neutral access/2: difficult access

0.780 0.415

Potential-

intermodal

1: truck�rail intermodal is considered always

or often as a potential transportation mode/0:

otherwise

0.349 0.477

Perishable 1: if the commodity is perishable/0: otherwise 0.160 0.367

Consolidation-

center

1: if the shipment has gone through a

consolidation center/0: otherwise

0.143 0.350

Distribution-

center

1: if the shipment has gone through a

distribution center/0: otherwise

0.270 0.445

Warehouse 1: if the shipment has gone through a

warehouse/0: otherwise

0.347 0.477

Decision-maker 1: if a 3PL company has made the shipping

decision/0: otherwise

0.104 0.305

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shows the final probit and logit models that estimate the probability of choosing

between truck and rail.Newey and McFadden (1994) and Train (2003) include detailed discussions on

binary choice models. Akaike and McFadden values are among many goodness-of-fit

measures offered for binary choice models, which were used along with the chi-squared

values for model selection (Train 2003). The higher the McFadden value and the lower

the Akaike measure, the better the explanatory power of the model. The McFadden

value is also known as the likelihood ratio index or pseudo R-squared and has a similar

range (0�1) as R-squared has in ordinary least square (OLS) models (Train 2003).

However, in general the McFadden values tend to be lower than the R-squares for the

OLS models. Standard t-statistics, shown in Table 3, are for testing whether each

coefficient has a non-zero effect on the choice probability. All the estimated parameters

in the final models are significant with a p-value of less than 0.05; most are significant

with 99% confidence interval. Wald, Likelihood Ratio, and Lagrange Multiplier tests,

known as Neyman�Pearson tests (Greene 2002), were also carried out to evaluate the

overall significance of the final models. Both models have pseudo R-squared values of

more than 57%, and correctly predict 95% of the observations. Percentage of correctly

predicted observations is usually high in binary choice models that predict a rare event.

The high percentage of correct predictions could be misrepresented as the general

explanatory power of the model. However, when the two possible outcomes are either a

Table 3. Mode choice models.

Probit model Logit model

Item Value t-ratio Value t-ratio VIF

Coefficient Constant �5.902* �6.050 �10.808* �5.696 �Distance 0.237E-03** 2.273 0.452E-03** 2.156 2.776

Weight 0.310E-04* 4.293 0.569E-04* 4.195 1.564

Truck-time 0.622* 5.019 1.110* 4.815 1.648

Rail-time �0.094* �2.579 �0.176** �2.295 2.387

Truck-cost-

index

0.388** 2.532 0.670** 2.361 3.408

Rail-cost-index �0.659* �3.474 �1.188* �3.331 1.099

Potential-

intermodal

1.214* 3.468 2.270* 3.265 2.776

Fit Measures Log likelihood �47.141 � �47.780 � �Model Chi-

squared

128.577 � 127.300 � �

Akaike I.C. 0.296 � 0.300 � �Pseudo R-

squared

0.577 � 0.571 � �

Correctly

predicted (%)

95.430 � 95.699 � �

Correctly

predicted (%) �only rail

72.727 � 72.727 � �

* Significant at 99% confidence interval.**Significant at 95% confidence interval.

862 A. Samimi et al.

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rare or common event, binary models tend to over-predict the latter, resulting in high

rates of correct predictions at the expense of largely ignoring the rare event outcomes.

For example, if 99 out of 100 choices are common and only 1 is a rare event, the model

can attain 99% accuracy by simply predicting all cases to be the former. Thus thepercentage of rare events that are correctly predicted is a more valuable measure of

predictive power for such models. In our case, choosing rail over truck could be

considered as a rare event with only around 9% chance of occurrence in this data. Both

models predicted more than 72% of rail shipments correctly, which is quite impressive

especially for a freight mode choice model.

Since the shipping cost and time of unobserved modes were imputed in a machine

learning module, it seemed necessary to control for potential multicollinearity

between explanatory variables. Although collinearity is unlikely to be a serious issuewhen all the coefficients are statistically significant in a binary choice model, very

large off-diagonal values were searched for in the variance-covariance matrixes as the

primary effect of multicollinearity. Variance inflation factors (VIF) were also

estimated for all the independent variables. Kutner et al. (2004) suggested a VIF of

5 as the threshold that indicates a presence of serious multicollinearity. For our

models, none of the variables had a VIF in excess of 3.5 (Table 3).

Analysis of results

During the model fitting process, many different combinations of the independentvariables were tested. We found that a broad range of variables influence the mode

selection process. This includes establishment-specific variables (e.g. establishment

size, location, access to rail and road network, decision-making unit in the supply

chain, etc.), shipment-specific variables (e.g. commodity type, value, weight, special

handling needs, etc.), and shipping mode-specific variables (e.g. cost, velocity,

reliability, safety, flexibility, etc.). These variables not only have significant impacts

on the mode choice, but also are interdependent. For instance, commodity type and

cost of shipment are correlated. Also, shipping cost and velocity are interdependent.This, in some ways, constrains the specification of the mode choice model, and

requires a close attention to address potential collinearity issue, which was discussed

in the previous section. This part of the paper analyzes and interprets the effect of

each explanatory variable in the final models, along with some other variables that

were found not to be significant in the final models but were shown to have

considerable effects on the choice of mode. A sensitivity analysis of mode choice on

fuel cost, which is a topic of considerable interest to the researchers and policy-

makers alike for obvious reasons, is also provided in this section.

General discussion

Marginal effect analysis was performed on the final models to provide a better

understanding of each explanatory variable’s impact on freight modal selection � andshown in Table 4. Although the values are similar for logit and probit models,

marginal effects have higher levels of significance in the logit model and thus the

discussion in this section will focus on it. Distance, weight, truck shipping time, rail

shipping time, truck cost index and rail cost index (see Table 1 for their definition)

turned out to be significant in the final model. DISTANCE has a positive sign

Transportation Planning and Technology 863

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indicating that rail is more likely to be chosen for long hauls. This finding is

intuitively interpretable and was also confirmed in former studies (Oum 1979). One

explanation for this trend is that rail shipments have a higher base price compared to

truck, which is diminished in long hauls. Weight of the shipment is another

significant variable in the model with a positive coefficient, indicating that larger

shipments are more likely to be transported by rail. This observation is also in line

with past studies. As indicated by Evers et al. (1996), past experiences with each

mode plays a determining role in the selection of mode. POTENTIAL-

INTERMODAL variable shows such effect in the models with positive coefficients,

indicating that firms that always or often consider truck�rail intermodality as a

possible option are more likely to select rail. Since in our model, the mode ‘rail’

includes shipments by rail alone or in combination with any other mode including

trucks, this finding is intuitive. This finding may seem trivial at first glance, but from

the modeling perspective the inclusion of such a variable makes other coefficients

more meaningful. For instance, shipping behavior of a firm preferring truck over rail

may be mistakenly attributed to the differences in cost and/or haul time, while the

real reason may have been that the shipper is unfamiliar with the rail mode in terms

of its service quality, cost and other factors. Therefore excluding such variables that

capture the effects of shippers’ knowledge or prejudice from the models may result in

erroneous interpretation of the coefficients.

Cost and haul time of each transportation mode are other significant factors in

mode selection. Having such mode-specific indicators enhances the explanatory

power of the model, especially when modeling freight transport behaviors.

A comparison between the coefficients of truck and rail transit time reveals that

the choice probability for truck is more sensitive to haul time than for rail. The

elasticities of truck and rail haul time, shown in Table 4, indicate that the effect of

truck travel time is almost 20 times greater for the truck mode. This shows that time

is a crucial factor especially when truck is preferred to rail. The cost index � which is

defined for each mode as the log of shipping cost divided by the product of haul time

and value of shipment � shows that the choice of rail is sensitive to cost. Shipping

cost is normalized over the shipping distance and value of the shipment in the

proposed cost index. Also, the log of this ratio conveys a non-linear behavior with

the attractiveness of each mode. Rail shipments’ sensitivity to the cost index is

around 1.7 times greater than that for truck shipments. An interesting observation in

the coefficients of time and cost variables is that shippers preferring truck are mainly

Table 4. Marginal effect analysis of the mode choice models.

Probit model Logit model

Variable

Marginal

effects Elasticity t-ratio

Marginal

effects Elasticity t-ratio

Distance 0.151E-05 1.244E-10 0.989 2.361E-06 1.943E-10 1.376

Weight 0.198E-06 7.665E-13 1.004 2.968E-07 1.150E-12 1.549

Truck-time 0.397E-02 1.749E-04 1.028 5.792E-03 2.554E-04 1.608

Rail-time �0.599E-03 �7.303E-06 �1.042 �9.183E-04 �1.119E-05 �1.457

Truck-cost-index 0.247E-02 �6.194E-05 0.964 3.497E-03 �8.759E-05 1.36

Rail-cost-index �0.420E-02 1.006E-04 �1.107 �6.200E-03 1.484E-04 �1.722

864 A. Samimi et al.

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concerned about shipping time and, in general, not overly sensitive to cost. On the

other hand, decisions on rail shipments are more sensitive to cost, but not to time.

This suggests that rail shipments are generally quite sensitive to cost and easily react

to changes in price. A more complete discussion on the effects of fuel pricefluctuation on modal selection is provided later in this section.

Other influential factors on mode choice

A variety of explanatory variables were considered in the modeling step. So far, we

have discussed the variables that were included in the final model. This does notnecessarily mean that other variables have no effect on modal selection. In most

cases, they cannot be in the model mainly due to interdependencies with other

variables. Two different tests of independence, Chi-square test and G-test, were

performed between shipping mode and other explanatory variables. Table 5 shows a

list of variables that were found to be dependent on transportation mode, according

to Chi-squared and G-squared values (Greene 2002).

Table 5 indicates the perishability of the commodity affects the choice of mode at

the 80% confidence level. This result has also been observed in past studies (Oum1979) and is mainly attributed to the effect of transit time on such commodities. Rail

shipments are more likely to go through a consolidation center, distribution center or

a warehouse, as suggested by 2nd, 3rd and 4th variables in Table 5. This could be

explained by size and distance of such shipments. Not a significant benefit is

obtained by sending small and short haul shipments to a consolidation or

distribution center (Higginson and Bookbinder 1994) resulting in such associations.

Two other variables, DECISION-MAKER and SAME-DECISION, were deployed

to capture the role of knowledge and previous experience of shippers about eachmode in the selection process. Shipments that are planned by a 3PL company, which

Table 5. Test of independence.

Mode

Chi-square

independence test

G-test of

independence

Variable Category 0 (%) 1 (%) Chi-squared p-value G-squared p-value

Perishable 0 77.1 6.9 1.847 0.174 1.663 0.197

1 13.8 2.2

Consolidation-center 0 79.9 5.8 15.532 0.000 12.050 0.001

1 10.8 3.5

Distribution-center 0 67.6 5.4 3.322 0.068 3.082 0.079

1 23.3 3.7

Warehouse 0 61.2 4.1 3.153 0.076 3.014 0.083

1 30.6 4.1

Decision-maker 0 83.4 6.2 4.452 0.035 3.566 0.059

1 8.6 1.8

Same-decision 0 4.9 1.6 8.463 0.004 6.100 0.014

1 86.3 7.1

Access 0 28.7 5.0 10.658 0.005 13.134 0.001

1 40.4 3.9

2 21.7 0.3

Transportation Planning and Technology 865

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usually have a broader knowledge of available modes and perform a more

comprehensive analysis for mode selection, are more likely to be shipped by rail.

This indicates that some shippers are not aware of rail benefits, and their decision

may be different if they had complete information about their alternatives. Also,3PLs may be able to combine shipments into a load that is large enough for a rail

shipment. Another variable showing a significant association with the choice of

mode is SAME-DECISION. Interestingly, shippers preferring the same mode they

would have used two years ago for a similar shipment are less likely to choose rail

over truck. This finding is in line with the aforementioned observation that persons

who had chosen truck were found not to consider rail as a potential transportation

mode in some cases. Simply put, trucking seems to have better customer loyalty. This

is perhaps a surprising finding considering the increase in the price of fuel that tookplace in the summer of 2008, less than a year before the survey. The last variable of

interest is the accessibility to intermodal terminals. Obviously, as the level of access

to rail or truck�rail intermodal facilities decreases, shippers prefer to use trucks.

Table 5 confirms such association at the 99% confidence level.

Mode choice and fuel cost fluctuation

Fuel price is an important component of freight transportation cost and has gonethrough large fluctuations in recent years. Its effects on the shipping behaviors are of

interest in many disciplines and are specifically looked into in this part of the paper.

Road freight demand is often considered much more inelastic to shipping cost than

passenger traffic, and there is a wide variation in the fuel cost elasticities estimated in

the past (Graham and Glaister 2004), although the variation is mainly due to the

difference in scope and method of studies.

Figure 1 illustrates changes in the share of rail freight transportation, when the

fuel price is increased by four different amounts. The binary logit model in Table 3 isused for this part of the analysis. In each case, 16 different possibilities are explored

in which the share of fuel price in the total shipping cost varies. Depending on

shipping distance, congestion level, fuel consumption of the fleet, topography, etc.,

the share of the fuel cost within the total cost of the shipment, and thus its possible

influence, varies. For instance, a long haul truck shipment, traveling through an

uncongested corridor, may be expected to be more affected by fuel price increases.

However, such conclusions require more investigation, since labor cost is by far the

largest part of trucking and is complicated to estimate, since some truckers are paidby the hour, some are paid by load, and some by miles driven. Therefore, estimating

share of the fuel cost in total shipping cost, just based on congestion level and

shipping distance, is not accurate.

The results of the analysis, shown in Figure 1, suggest that freight modal

decisions are very much inelastic to fuel cost and do not change significantly with

even a 50% increase in fuel cost. When the fuel price doubles, however, shippers start

shifting to rail when fuel cost accounts for a large portion of the total cost. This may

happen in long haul shipments that experience a relatively low level of congestion.Figure 1 also explores two other scenarios with 150 and 200% increases in fuel cost.

In these scenarios, around 7% of total shipments are expected to shift to rail when

the fuel price is a major component of total shipping cost. However, even when the

fuel cost is not a large factor, a significant shift of around 3% is expected.

866 A. Samimi et al.

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The low elasticities of modal decisions with respect to fuel cost that were

obtained in this study are in line with many other studies in which such decisions

were introduced as inelastic or in best cases much less elastic than passenger

transportation (Graham and Glaister 2004).

Conclusions

Behavioral freight mode choice models are of great importance for both academia

and practice (for example, policy-making). However, it has been mainly overlooked

in freight demand modeling primarily due to data limitations. In this paper, we

presented the development of binary mode choice models between truck and rail

(including intermodal modes) based on the data obtained from an online national

level freight survey conducted recently in the US by the research team (Samimi et al.

2010a). This study modeled modal selection decisions as part of a microsimulation

framework, which also shed light on modal selection behavior.

40%30%20%10%

Around 3% reduction in rail share. Around 3% increase in rail share.

Rail share is almost unchanged. Around 7% increase in rail share.

50% increase in fuel price150% increase in fuel price

200% increase in fuel price

Fuel share in total truck costFuel share in total truck cost

Fuel share in total truck cost Fuel share in total truck cost

Fuel

sha

re in

tota

l rai

l cos

tFu

el s

hare

in to

tal r

ail c

ost

Fuel

sha

re in

tota

l rai

l cos

t

100% increase in fuel price

Fuel

sha

re in

tota

l rai

l cos

t

40%30%20%10%

1%

3%

5%

10%

40%30%20%10%

1%

3%

5%

10%

40%30%20%10%

1%

3%

5%

10%

10%

3%

50%

10%

Figure 1. Freight mode sensitivity to fuel price fluctuation in different scenarios.

Transportation Planning and Technology 867

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Two binary choice models were developed to broaden the understanding of the

mode selection behavior by shippers, 3PLs, and receivers in the US freight markets.

We used the machine-learning approach to generate the time and cost of shipments

by modes that were not chosen by the respondents. Of the shipment-specific

variables, distance, weight and value of commodity were found to be significant. It

was also found that the truck shipments were extremely sensitive to travel time and

rail shipments sensitive to cost. We found that familiarity with a mode, especially

trucking, also had a strong influence on mode choice behaviors. Other variables were

found to have significant correlation with mode choice, although they could not be

included in the model due to interdependency issues. For example, the perishability

of the commodity, access to intermodal facility, and having a 3PL as the decision-

maker all seem to affect the mode choice.

Analysis of various scenarios involving large increases in fuel price revealed that

mode choice is not particularly sensitive to fuel price � up to a point. Our analysis

showed that even a 50% increase in fuel price did not cause any significant modal

shift between truck and rail. However, when the increase reaches 150 and 200%,

around a 7% shift from truck to rail shipments can be expected.

The findings from this study are generally in line with past efforts by other

researchers. However, some of the findings, especially the effect of the variables

related to the decision-maker, such as the past experience and familiarity with modes,

are unique and provide valuable insights into the mode choice behaviors. Also, the

mode-specific characteristics of time and cost of shipments, made possible by the

application of a machine-learning technique, enabled a more comprehensive analysis

with respect to those two variables than before.

As a final note, to the best of the authors’ knowledge there is no robust and

comprehensive longitudinal study of the effect of fuel cost on freight mode choice

decisions. This can be attributed primarily to the fact that disaggregate freight data

are so rare and expensive to collect that the researchers have been forced to limit their

study to a cross-sectional data. This obviously applies to the present study. It is our

hope that we will be able to address such a gap in the near future.

Acknowledgements

The authors appreciate the assistance of the National Center for Freight, Infrastructure,Research and Education (CFIRE) at the University of Wisconsin-Madison and the IllinoisDepartment of Transportation for funding this study. All responsibility for the content of thepaper lies with the authors.

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