320

World Review of Intermodal Transportation Research, Vol. 3, No. 4, 2011

Measurement of energy-saving effect by intermodal
freight transport in Thailand
Shinya Hanaoka*
Department of International Development Engineering,
Graduate School of Science and Engineering,Tokyo Institute of Technology,
2-12-1-I4-12, O-okayama, Meguro-ku,
Tokyo 152-8550, Japan
Fax: +81-3-5734-3468
E-mail: hanaoka@ide.titech.ac.jp*Corresponding author

Taqsim Husnain
Department of Civil and Environmental Engineering,
The George Washington University,
725 23rd Street, N.W.,
Washington, DC 20052, USA
E-mail: taqsim14@gwmail.gwu.edu

Tomoya Kawasaki
Department of International Development Engineering,
Graduate School of Science and Engineering,Tokyo Institute of Technology,
2-12-1-I4-12, O-okayama, Meguro-ku,
Tokyo 152-8550, Japan
Fax: +81-3-5734-3468
E-mail: kawasaki@tp.ide.titech.ac.jp

Pichet Kunadhamraks
The Office of Transport and Traffic Policy and Planning,
Ministry of Transport,
35 Petchaburi Road, Ratchathewi,
Bangkok 10400, Thailand
Fax: 662/2151469
E-mail: pichet.k@mot.go.th
Abstract: In Thailand, transport sector is the largest energy consuming sector(38%). Road haulage of freight transport accounts for approximately 92%
of total domestic freight movements. Accordingly, it is one of the largest
contributors to adverse environmental impacts. This study presents one option
to reduce energy consumption through modal shift from trailer to intermodal

Copyright © 2011 Inderscience Enterprises Ltd.

Measurement of energy-saving effect by intermodal freight transporttransport involving railway and waterway. It focuses on freight movements
between Bangkok and Hat Yai in Thailand. Energy savings are measured
by multi-objective optimisation model using decision variables consistingof three mode options: trailer only, intermodal-rail and intermodal-waterway.
In addition to energy consumption, the objective function also includes timeand charge of shipment factor.
Keywords: intermodal transport; energy consumption; multi-objective
optimisation model.
Reference to this paper should be made as follows: Hanaoka, S., Husnain, T.,
Kawasaki, T. and Kunadhamraks, P. (2011) ‘Measurement of energy-saving
effect by intermodal freight transport in Thailand’, World Review of Intermodal
Transportation Research, Vol. 3, No. 4, pp.320–337.
Biographical notes: Shinya Hanaoka is an Associate Professor in the
Department of International Development Engineering, Graduate School of
Science and Engineering, Tokyo Institute of Technology, Japan. He received a
PhD, Master’s and Bachelor’s Degrees from Tohoku University, Sendai, Japan.
He is a Visiting Associate Professor of School of Engineering and Technology,
Asian Institute of Technology (AIT), Thailand. He worked as a Researcher
in Institute for Transport Policy Studies at Tokyo (1999–2003), a Visiting
Researcher in Institute for Transport Studies, The University of Leeds, UK(2002). His research interests include transport logistics, air transport andtransport project management.
Taqsim Husnain is a PhD Candidate in the Department of Civil andEnvironmental Engineering, The George Washington University, USA.
He has done his Masters Degree in Transportation Infrastructure and Systems
Engineering from the Civil and Environmental Engineering Department in
Virginia Tech, USA in 2009. He has also received a MEng Degree from Asian
Institute of Technology (AIT), Bangkok, Thailand in 2006 and BSc from
Bangladesh University of Engineering and Technology, Bangladesh in 2004.
He has done researches in public transportation planning, route choicemodelling, systems optimisation and intermodal transport.
Tomoya Kawasaki is a PhD Candidate in the Department of International
Development Engineering, Graduate School of Science and Engineering,Tokyo Institute of Technology, Japan, and also completing his researchin Institute for Transport Planning and Systems (IVT), Swiss Federal Instituteof Technology, Zürich (ETH Zürich), Switzerland. He received a Degreeof MEng from Asian Institute of Technology (AIT), Bangkok, Thailand, in2008 and BEng from Nihon University, Tokyo, Japan, in 2006. His researchinterests are transport logistics, intermodal transport and maritime transport.
Pichet Kunadhamraks served as a Senior Civil Engineer with the Officeof Transport and Traffic Policy and Planning, Ministry of Transport.
His responsibilities are auditing the projects and set up the master plan for thenationwide rail network development. He received a Degree of Doctor
of Engineering from Asian Institute of Technology (AIT), Bangkok, Thailand,in 2007. His publications involved in evaluating the logistics performanceof intermodal transportation in Thailand.

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1

S. Hanaoka et al.

Introduction

Domestic freight movement in Thailand in 2005, calculated on the basis of tonnage,
was proportioned as 92.8% by road, 5.1% by waterway (river and coastal), 2.1%
by rail and 0.01% by air (MOT, 2005). The prevalence of road transport resulted
in various problems, such as traffic congestion, noise, vibration, and emissionsof NOx and CO2. The transport sector is the most energy-consuming sector (38.3%)
in Thailand (EPPO, 2006), and almost 80% of energy consumption in this sector is used
for land transport, where 78.6% of energy is consumed by cars and trucks and only
0.5% of energy is consumed by rail. Waterway transport accounts for only 4.6%of the total consumption, and the remaining 16.3% is associated with air transport(EPPO, 2003).
Road haulage is the most energy-consuming mode of freight transport, compared withtransport by rail or water, in terms of energy efficiency per ton-km. The energy consumed
for the shipment of goods by rail or barge is substantially less than that consumed
by truck. For instance, in the USA, the energy consumption of truck transport, in termsof joule per ton-km, is approximately 12 times higher than that of rail or waterway
transport (IEA, 2001). Shinke et al. (2007) quantified the reduction of CO2 emission
resulting from a shift from truck to rail and waterway transport in Japan. It was revealed
that under these circumstances, CO2 emission was reduced by 2.8% for rail transportand by 1.8% for waterway transport. However, studies to date neglect other importantfactors such as the transport time and the shipment charge when estimating energy
saving. Despite the recent emphasis on making transport more environmentally friendly,
little research has examined the relation between logistical structure and the choiceof mode of transport (McKinnon, 1999). To examine energy saving in intermodaltransport, we should consider this problem from a multi-objective point of view.
There have been no relevant studies on intermodal transport in Thailand except
by Kunadhamraks and Hanaoka (2008), who developed a method for evaluating
its performance.
The objectives of this study are•

to develop an optimisation model for estimating the modal share in the context
of a multi-objective optimisation problem•

to measure consequences for energy saving by comparing an estimated modal sharewith the actual modal share.

To measure energy saving, energy consumption in modes of transport with different
degrees of energy efficiency, such as trailer, rail and waterway transport,
must be analysed in terms of identical Origin and Destination (OD) and cargo volume
parameters. This study focuses on the route between Bangkok and Hat Yai, wheretrailer-only, rail-intermodal and waterway-intermodal transport are available. In general,the logistics companies (e.g. freight forwarders) do not consider energy consumption as acriterion for the choice of transport mode. Thus, the optimal modal share is determined
here using a multi-objective optimisation model to minimise energy consumption,transport time and shipment charge.

Measurement of energy-saving effect by intermodal freight transport

2

323

Model

2.1 Formulation of the multi-objective optimisation problem
Logistics companies were queried to determine which factor among transport time,
shipment charge, security and reliability was considered to be the most important factor
for choosing a particular mode of transport (details are given in Section 3).
The shipment charge and the transport time were identified as the two most importantfactors. The model in this study is formulated as a multi-objective optimisation problem
with the following three variables: energy consumption, transport time and shipment
charge. Therefore, the model has three objective functions – minimising the total energy
consumption, the total transport time and the total shipment charge – for a given cargovolume from the origin to the destination.
The optimisation model developed here was inspired by earlier work, in particular
IEE (1993), Greene and Fan (1994) and Schipper et al. (1997), which focused onenergy consumption for the movement of a given cargo volume in terms of ton-km.
The optimisation model is formulated as follows:
min f1 = ∑∑ µ jk × s j × d jk × W × α jk × β jk
j

(1)

k

min f 2 = ∑∑
j

k

d jk
v jk

× s j × W × σ jk × ρ jk + ∑∑ D jk , j ( k +1) × s j × W
jmin f 3 = ∑∑ c jk × s j × d jk × W + ∑∑ C jk , j ( k +1) × s j × W
j

k

j

(2)

k −1

(3)

k −1

s.t.

∑s =1
j

(4)

s j ≥ 0 ∀j

(5)

j

where
f1:
f2:
f3:
j:
k:
µjk:
νjk:
cjk:
djk:
W:
αjk:
βjk:

Total energy consumption (BTU)
Total transport time (ton-h)
Total shipment charge (Baht)
RouteTransport mode
Energy efficiency (BTU/ton-km)
Travel speed (km/h)
Unit shipment charge (Baht/ton-km)
Distance travelled (km)Total cargo volume (ton)Energy consumption adjustment factor for load
Energy consumption adjustment factor for Empty Haulage

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S. Hanaoka et al.

σjk:
ρjk:
Djk,j(k+1):
Cjk,j(k+1):
sj:

Transport time adjustment factor for load
Transport time adjustment factor for Empty Haulage
Transhipment time at seaport(s)/rail terminal(s) (h)Shipment charge at seaport(s)/rail terminal(s) (Baht/ton)
Modal share of route j.

2.2 Objective functions
The first objective function is to minimise the total energy consumption (f1) for the
shipment of a given cargo volume from its origin to its destination along different routes.
Only energy consumption for shipment by the actual transport modes is considered here,
ignoring other contributions to energy consumption at seaports or rail terminals
associated with handling, loading and relocations of containers owing to the limitation ofdata availability. The total energy consumption is measured in British Thermal Units(BTU). The second objective function is to minimise the total transport time (f2) for aparticular mode. Transhipment time at seaports or rail terminals is also considered.
The total transport time is the summation of the shipment time and the transhipment timethrough different modes k and (k + 1). The unit of total transport time is the ton-h, whichallows the transport time to be measured in terms of the unit ton. The third objectivefunction is to minimise the total shipment charge (f3) for the shipment of a given
cargo volume from one origin to one destination by different routes. It includes thetariff/charge incurred at seaports or rail terminals and is measured in Thai Baht
(1 USD = 35.18 Baht, as of January 2009).

2.3 Input parameters2.3.1 Energy efficiency (µjk)
The energy efficiency is estimated for each mode on the basis of the energy used per
ton-km of freight carried. This is done by estimating the energy used and dividing itby the tonnage carried times the distance covered in kilometres (DOE, 1982). In thisstudy, the energy efficiency of a particular mode is given byµ jk =

139, 000,
3.7854 × e jk × V jk

(6)

where
Energy efficiency (BTU/ton-km)
µjk:
ejk:
Fuel efficiency (km/litre)
Vjk:
Average shipment volume (ton)
1 gallon (US) = 3.7854 l, 1 gallon of diesel = 139,000 BTU.

2.3.2 Travel speed (νjk)
The travel speed can be defined as the distance travelled by a particular mode over a unit
length of time. In this study, the travel speed by different types of modes is given byMeasurement of energy-saving effect by intermodal freight transport
v jk =

d jk

325
(7)

t jk

where
Travel speed (km/h)
νjk:
djk:
Distance travelled (km)tjk:Transport time (h).

2.3.3 Unit shipment charge (cjk)
The shipment charge is the charge paid by clients to logistics companies for the shipmentof a given cargo volume from its origin to its destination. The unit shipment charge
is calculated as the shipment charge divided by the tonnage carried times the distancecovered in kilometres, as expressed in equation (8). In Thailand, it is widely known thatthe depreciation cost of an automobile, which may include a trailer, is low, and this isalso true for rolling stock and barges, which can be used for 30–40 years on average.
Thus, in this study, the fixed cost for each haulage is ignored.
c jk =

B jk

(8)

d jk

where
Unit shipment charge (Baht/ton-km)
cjk:
Bjk:
Shipment charge (Baht/ton)
djk:
Distance travelled (km).

2.3.4 Energy consumption adjustment factor for load (αjk)
Even in the same transport mode, energy consumption depends on the loading conditions.
In this study, the Load Factor (LF) and Empty Haulage (EH) are taken into account forassessing the impact of energy consumption on the results. The LF is one of the measures
for the operating efficiency, which is used in logistics to determine the utilised capacity
percentage (equation (9)). It is the ratio of the average load to the total vehicle freightcapacity, expressed in tones or volume. Energy consumption and, consequently, energy
efficiency vary with the LF. Thus, it is necessary to determine the adjustment factor
to measure the energy efficiency more accurately.
LF =

V jk
M jk

,

where
LF:
Load Factor (%)
Mjk: Loading capacity of freight (ton).

(9)

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S. Hanaoka et al.

Fuel efficiency (ejk) is approximately proportional to the weight of the vehicle (NHTSA,
2003). This means that the weight is one of the most significant variables that determine
fuel efficiency. On the basis of this concept, the energy efficiency (µjk) of different LF
can be determined by considering a linear relationship between fuel efficiency (km/l) andthe LF. In case, the LF is 100%, the energy consumption adjustment factor would be 1.0.
The energy consumption adjustment factor is defined in equation (10). As the LF
decreases, the energy efficiency also decreases. Adjustment factors are determined foreach type of transport mode.

α jk =

µ any
jk
full
µ jk

(10)

where

αjk:

Energy consumption adjustment factor for load

µ : Energy efficiency at any load (BTU/ton-km)
anyjkµ fullEnergy efficiency at full load (BTU/ton-km).
jk :

2.3.5 Energy adjustment factor for Empty Haulage (βjk)
EH is equivalent to the case where the LF is 0, which is normally a consequence of an
unbalanced supply and demand relationship. As the number of EH increases, the energy
efficiency per ton-km decreases. Thus, the proportion of EH is used as anotheradjustment factor. EH can be defined as the percentage of times that a vehicle returns
with an empty cargo between OD. In this study, the proportion of EH was calculatedusing the concept outlined in Figure 1 and equation (11). An EH of 100% means thevehicle returned empty every time. In short, EH equals 1.0 when, for a given round trip,
the vehicle is empty on the return journey. Similarly, EH equals 0.5 when the vehicle isempty on only one return journey for two round trips.
EH =

where
EH:
re
jk

N :

N rejkN round
jk

× 100

(11)

Empty Haulage (%)
Number of EH on return haulage (times)

N round: Number of round trip between OD (times).
jk

Equation (12) determines the energy consumption adjustment factor for EH byconsidering the fuel efficiencies (km/l) of a Full Container Load (FCL) and for an empty
load. Thus, βjk is 1.0 when EH is 0%. The function βjk, formulated as energy efficiency,
becomes lower when EH increases.

β jk = 1 +µ full
EH / 2jk×,µ empty− (EH / 2)
100
jk

(12)

Measurement of energy-saving effect by intermodal freight transport
where
βjk:

Energy consumption adjustment factor for Empty HaulageµFuel efficiency at any load (BTU/ton-km).

empty
jk

:

Figure 1327

How to calculate Empty Haulage

2.3.6 Transport time adjustment factor for load (σjk) and Empty Haulage (ρjk)
The travel speed of a vehicle is also affected by volume of its cargo. In other words,
transport time also varies with the LF. Since an empty trailer and a trailer with LF lessthan 1.0 is more likely to move faster than a trailer with FCL, the adjustment factor forthe load becomes necessary to obtain a more accurate transport time. Thus, in this study,
it is assumed that the travel speed of a trailer in the condition of EH is 20% fastercompared with FCL. Similarly, rail and waterway vehicles are 10% faster than thoseunder FCL condition. Then, the transport time adjustment factor is determined by theassumption that the change in the travel speed of each mode is proportional to the cargoweight.

2.3.7 Transhipment time at seaport(s)/rail terminal(s) (Djk,j(k+1))
In the case of intermodal transport, the transhipment time for handling, storage, stuffing
and relocation of the containers at seaports or rail terminals must be considered. In thisstudy, Djk,j(k+1) indicates the transhipment time incurred for transferring the container
at the point of interchange between the modes k and k + 1 on route j. This is measured
by the hour.

2.3.8 Shipment charge at seaport(s)/rail terminal(s) (Cjk, j(k+1))
The shipment charge at seaports and rail terminals depends on many factors and differsfrom one seaport or rail terminal to the next. A tariff can be charged either per container(20′, 40′ or 45′) or per ton of freight. Import and export shipment charges are also
different. For the purpose of maintaining the consistency of the objective functions,328

S. Hanaoka et al.

shipment charge at seaports and rail terminals includes only handling charge in Baht/ton.
Cjk,j(k+1) represents the shipment charge incurred for transferring the container at the pointof interchange between the modes k and k + 1 on route j. Although, in general, additionalcosts of labour and inventory will be required for longer transhipment times, for example
as a result of a delay, this is not considered in this study.

2.4 Constraints
Two constraints are incorporated within the objective functions. The first constraint(equation (4)) is used to ensure that the sum of modal shares via different routes must
always be equal to one. The second constraint (equation (5)) is used for sign restriction toensure non-negative values for all modal shares.

2.5 Output (sj)The output of the model gives Pareto-optimal solutions for the modal shares. This paper
also focuses on the effectiveness of energy saving of each Pareto-optimal solution, whichcannot be better off without making some other value worse off (Collette and Siarry,2003). The sj indicates the optimal modal share for route j.
Optimisation is done with WWW-NIMBUS, a Non-differentiable Interactive
Multi-objective Bundle-based optimisation System (Miettinen and Mäkelä, 1999, 2006).
With this interactive web-based software, the user can define the problem to be solved
and set preferences in a flexible way, by changing the beginning and end conditions
to generate a number of optimal solutions. Results are generated by a local solverusing a proximal bundle method and global solvers using Genetic Algorithms (GAs).
The structure of the interactive NIMBUS algorithm does not limit the variety
of its applications in the area of multi-objective optimisation. An implementation
WWW-NIMBUS (Miettinen and Mäkelä, 2000) of the NIMBUS method is available
on the internet at http://nimbus.mit.jyu.fi/. WWW-NIMBUS contains two variant GAs
with different constraint-handling techniques (Miettinen et al., 2003). One of thesetechniques is based on adaptive penalties, and the other technique can be called a methodof parameter-free penalties.

3

Numerical results

3.1 Data collection and parameter setting
A questionnaire survey was conducted to investigate preferences and behaviours ofdifferent types of logistics companies in Thailand related to domestic freight transport.
These companies included freight forwarders, transport operators and shippers.
The questionnaire (in Thai language) was distributed to 300 container cargo companies
located in the vicinity of Bangkok, close to Laem Chabang Port and Lat KraBang Inland
Container Depot (ICD) in the Bangkok suburbs. This is the only area where intermodaltransport is actively used in Thailand.1 The survey was carried out by a number ofmethods, including face-to-face interviews, telephone interviews, postal delivery, fax ande-mail in the period between December 2005 and January 2006. Most of the data wereobtained by face-to-face and telephone interviews. Other communication methods (postal

Measurement of energy-saving effect by intermodal freight transport329

delivery, fax and e-mail) yielded much fewer respondents. From 300 sets of distributed
questionnaire, 137 sets were returned, from which 110 (48 shippers, 32 carriers and
30 freight forwarders) were selected by filtering out incomplete and unreliable sets.
Most of the non-respondents had been sent the questionnaires by post, fax and e-mail.
On the other hand, the face-to-face and telephone interviews generally gave a highresponse rate. However, the type of respondents has no relationships with the numberof non-respondents.
The questionnaire consisted of questions regarding the cargo volume for eachtransport mode, the significant factor for choosing a particular transport mode, thefrequency of use for each type of trailer (6 wheels, 8–10 wheels and 18 wheels), theproportion of EH of a trailer, the fuel efficiency for each type of trailer, the distribution
pattern of representative transport modes (either using intermodal transport or not), thetravel speed of haulage, and the transhipment time and shipment charge at the seaports
and rail terminals. For the purpose of checking the reliability of the data, an additionalquestionnaire survey was conducted with another set of companies, which resulted
in 62 valid samples. A t-test was performed between the first 110 samples and second setof 62 samples with a significance threshold of 5% (two-tailed). There was no significant
difference between the sample populations, which confirms the stability of the data.
The questionnaires showed the modal share for the logistics companies interviewed
distributed as 77.3% for trailer, 13.9% for rail and 8.8% for waterway transport, in termsof metric tons. Rail transport had a much higher modal share in the area studied thanin the whole of Thailand. The weighted average of EH for trailers was 47.4% (shipper
51.0%, carrier 45.9% and freight forwarder 43.1%).

3.2 Case study: Route between Bangkok and Hat Yai in Thailand
A case study of the route from Bangkok to Hat Yai was carried out to estimate the impact
of intermodal transport on energy saving. Figure 2 shows a map of southern Thailand,
which includes Bangkok and Hat Yai. Within this network, three transport modes
are available: trailer-only (Route 1), intermodal-rail (Route 2) and intermodal-waterway(Route 3). On the basis of the questionnaire survey, the maximum load for 6- and18-wheel trailers are 10.60 tons and 36.40 tons, respectively. These three routes areshown in Figure 3 as a route network. According to the interview survey to the Ministryof Transport (MOT) of Thailand, the actual modal share for 2008 on this route was
94.8% for trailers, 4.7% for coastal shipping, 0.3% for rail and 0.2% for air transport.
Types of the commodity are mainly rice, fish and vegetable, which are classified asclass 4 (MOT, 2006). Containers may be used for transporting rice and vegetables,
but not for fish. On this route, several products other than rice, vegetable and fish aretransported, such as sugar and rubber. The unit shipment charge for rail is determined byassuming the transport of class-4 products for convenience.
Parameter values for energy efficiency, the energy consumption adjustment factor,
travel speed and transhipment time at seaport and rail terminal are derived fromthe questionnaire survey, whereas other parameters are set from the interview survey
and literature-based survey results (MOT, 2001; PDP Australia Pty Ltd/Meyrick andAssociates, 2005; SRT, 2004). The data set is summarised in Table 1.

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S. Hanaoka et al.

Table 1

Data sets of parameters for the calculation

Route 1: Trailer-only
Cargo volume=200 ton

Energy efficiency=480.25 BTU/ton-km

Distance=926 km

Energy consumption adjust. Factor (load)=1.45

Energy consumption adjust. Factor (EH)

=1.23

Travel speed=43.60 km/h

Transport time adjust. Factor (load)=0.93

Transport time adjust. Factor (EH)

=1.28

Unit shipment charge=0.93 Baht/ton-km

Route 2: Intermodal-rail
Cargo volume=200 ton

Energy efficiency (trailer)=1,106.76 BTU/ton-km

Distance (trailer)=27 kmEnergy consumption adjust. Factor (trailer, load)=1.25

Energy consumption adjust. Factor (trailer, EH)=1.23

Energy consumption adjust. Factor (rail, load)=1.09

Energy consumption adjust. Factor (rail, EH)=1.05

Energy efficiency (rail)=650 BTU/ton-km

Distance (rail)=945 km

Travel speed (trailer)=41.1 km/h

Transport time adjust. Factor (trailer, LF)=0.94

Transport time adjust. Factor (trailer, EH)=1.28

Transport time adjust. Factor (rail, LF)=0.96

Transport time adjust. Factor (rail, EH)=1.03

Travel speed (rail)=59.51 km/h

Transhipment time (rail terminal)=20 h

Unit shipment charge (trailer)=5.04 Baht/ton-km

Unit shipment charge (rail)=0.46 Baht/ton-km

Shipment charge (rail terminal)=(45 + 50) Baht/ton

Route 3: Intermodal-waterway
Cargo Volume=200 ton

Energy efficiency (trailer)=1,106.76 BTU/ton-km

Distance (trailer)=7 + 35 km

Energy consumption adjust. Factor (trailer, load)=1.25

Energy consumption adjust. Factor (trailer, EH)=1.23

Energy consumption adjust. Factor (barge, load)=1.43

Energy consumption adjust. Factor (barge, EH)=1.17

Energy efficiency (barge)=260 BTU/ton-km

Measurement of energy-saving effect by intermodal freight transport
Table 1

Data sets of parameters for the calculation (continued)

Route 3: Intermodal-waterway
Distance (barge)=798 km

Travel speed (trailer)=41.1 km/h

Transport time adjust. Factor (trailer, load)=0.94

Transport time adjust. Factor (trailer, EH)=1.28

Transport time adjust. Factor (barge, Load)=0.95

Transport time adjust. Factor (barge, EH)=1.17

Travel speed (barge)=25.37 km/h

Transhipment time (seaport)=12 hUnit shipment charge (trailer)=5.04 Baht/ton-km

Unit shipment charge (barge)=0.44 Baht/ton-km

Shipment charge (seaport)=(77 + 68) Baht/ton

Substituting parameters for objective function
f1 = 480.25 * 926 * 1.45 * 1.23 * 200 * s1
+ (1,106.76 * 27 * 1.25 * 1.23 + 650 * 945 * 1.09 * 1.05) * 200 * s2
+ (1,106.76 * (7 + 35) * 1.25 * 1.23 + 260 * 798 * 1.43 * 1.17) * 200 * s3
f2 = 926/43.6 * 0.93 * 1.28 * 200 * s1
+ (27/41.1 * 0.94 * 1.28 * 945/59.51 * 0.96 * 1.03 + 2 * 20) * 200 * s2
+ ((7 + 35)/41.1 * 0.94 * 1.28 * 2 + 798/25.37 * 0.95 * 1.17 + 2 * 12) * 200 * s3
f3 = 0.93 * 926 * 200 * s1
+ (5.04 * 27 + 0.46 * 945 + 45 + 50) * 200 * s2
+ (5.04 * (7 + 35) + 0.44 * 798 + 77 + 68) * 200 * s3
Figure 2Southern part of Thailand including Bangkok and Hat Yai

331

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S. Hanaoka et al.

Figure 3

Route network between Bangkok and Hat Yai

On the basis of the interview survey, the 18-wheel trailer was selected for route 1 withthe trailer-only case, whereas the 6-wheel trailer was used for intermodal-rail
and intermodal-waterways by means of the access and egress transport mode of bothintermodal-rail and intermodal-waterway. The distance between Hat Yai railway terminal
and the city centre of Hat Yai is very short (0.2 km) and was assumed to be 0 km forconvenience. The analysis considered the shipment of 200 tons from Bangkok to Hat Yai.
The LF and EH of the trailers were set on the basis of the interview survey.
The data in Table 1 were input in WWW-NIMBUS to generate Pareto optimal
solutions for the modal share transported by routes 1–3. WWW-NIMBUS performs the
optimisation by giving priority to a particular objective function. In this study, theminimisation of energy consumption was given priority. Pareto-optimal solutions ranging
from the base solution (alternative 1) to the solution for minimum energy consumption(alternative 8) are summarised in Table 2.2 The base solution here represents the“solution that is at the median between the maximum and the minimum value among thePareto-optimal solutions which can be generated”.
Table 2

Pareto-optimal solutions for energy minimisation
Modal Share (%)

Objective functions

Alternatives

Route 1(s1)

Route 2(s2)

Route 3(s3)

Energy(BTU × 106)Time
(ton-h × 103)

Charge(Baht × 103)

1 (base)

44.98

10.61

44.41

124.54

8.69

154.40

2

38.55

9.10

52.35

118.76

9.13

152.54

3

32.13

7.58

60.29

112.979.56

150.68

4

25.70

6.06

68.24

107.1910.00

148.83

5

19.28

4.55

76.17

101.41

10.43

146.97

6

12.85

3.03

84.12

95.62

10.87

145.11

7

6.43

1.52

92.05

89.84

11.30

143.26

8

0

0

100

84.06

11.74

141.40

As for the change of alternative solutions, transport time would increase and shipment
charge would decrease as energy consumption is reduced. On the other hand, there is atrade-off between energy consumption and transport time; the shipment charge is
proportional to energy consumption. To minimise energy consumption, the transport timeMeasurement of energy-saving effect by intermodal freight transport333

should be increased. The modal share of the basic solution was calculated as follows:
trailer-only (route 1) 45%, intermodal-rail (route 2) 11% and intermodal-water (route 3)
44%, respectively. The actual modal share for a trailer in 2008 was 94.8% as mentioned
before. This study compares the actual modal share3 with each Pareto-optimal solution
for the purpose of assessing the effectiveness of energy savings and the corresponding
value of other objective functions. This is summarised in Table 3.
Table 3Comparison with actual modal share

Alternatives

Energy(BTU × 106)

Change(%)

Time(ton-h × 103)

Change(%)

Charge(Baht × 103)

Change(%)

1 (base)124.54–25.64

8.69

37.74

154.40–17.562

118.76–29.10

9.13

44.72

152.54–18.55

3

112.97–32.55

9.56

51.53

150.68–19.54

4

107.19–36.00

10.00

58.51

148.83–20.53

5

101.41–39.45

10.43

65.32

146.97–21.536

95.62–42.91

10.87

72.30

145.11–22.52

7

89.84–46.36

11.30

79.11

143.26–23.518

84.06–49.81

11.74

86.09

141.40–24.50

It is shown that a 25% reduction of energy consumption can be achieved by utilisingintermodal transport in the base case. For the alternative solutions, energy consumption
is considerably reduced by the increase in the modal share of intermodal-waterway
transport. From these results, it can be concluded that modal shift from trailer tointermodal transport is quite effective in reducing energy consumption. However,alternative 8, which is a solution for minimum energy consumption, leads to a 100%
share for intermodal-waterway transport. This means that intermodal-waterway transport
is preferable for minimising energy consumption; however, it is not a viable solution.
In Table 3, the transport time, which shows a trade-off with energy consumption, is 37%
longer compared with the actual modal share in the base case and also in otheralternatives. Although intermodal transport is quite effective in reducing energy
consumption, it should be treated as a trade-off relation. The sensitivity of transport timeis relatively significant against energy consumption, whereas of shipment charge isrelatively small. It is, therefore, important to take into account the transport time inminimising energy consumption.

3.3 Sensitivity analysis
Several parameters that were used as input for the model were assigned values as realistic
as possible. Nevertheless, results of calculation are affected by the scale of numericalvalues. Thus, in this study, sensitivity analysis was conducted for observing the modal
share as a result of changing three parameters, such as the travel speed of a trailer,
the shipment charge at a seaport, and the transhipment time at a seaport to assess thereliability of the calculation. This analysis was done for the base case (alternative 1).
The sensitivity to change in the travel speed of a trailer is shown in Figure 4.
The horizontal and vertical axes indicate the percentage change in travel speed of a trailer

334

S. Hanaoka et al.

and the modal share of each route, respectively. As the travel speed is decreased, a slight
reduction of the modal share on route 1 (truck only) is observed. However, there is onlya small decrease of 0.103% in the modal share over route 1 for a 20% decrease in trailer
travel speed. Thus, modal share is not significantly affected by the change in trailer travel
speed.
Figure 4

Change in each modal share by change in travel speed of trailer

With regard to the shipment charge at seaports, the sensitivity of the modal share for eachroute was analysed over a wider range of values from –50% to +50%. Results are shownin Figure 5. The sensitivity for intermodal-rail transport (route 2) is higher than for othermodes (routes 1 and 3). The modal share for route 2 is 18.16% (a +7.55% increase) for a
50% increase in the shipment charge at seaports. On the other hand, the modal share forroute 2 reaches almost 0% for a 30% reduction. Subsequently, the fluctuation of theseaport charge has an effect on the modal share of rail-intermodal transport. Nevertheless,the order of the modal share for each route is not changed at all within the range of values
in the analysis.
Figure 5

Change in each modal share by change in shipment charge at seaportMeasurement of energy-saving effect by intermodal freight transport

335

Figure 6, which depicts the modal share in case the transhipment time at seaportis changed, shows that intermodal-waterway transport (route 3) slightly overtakes
trailer-only transport (route 1) at the point of 12% reduction and 9% increase intranshipment time at seaport. However, this sensitivity in terms of modal share is not veryhigh. Therefore, the above-mentioned three sensitivity analyses conclude that the results
are relatively stable and are hence very likely to be reliable.
Figure 6

4

Change in each modal share by change in transhipment time at seaport

Conclusion

This study focuses on•

the development of a multi-objective optimisation model to determine the optimised
modal share through three different modes to minimise the total energy consumption,
the total transport time and the total shipment charge•

the measurement of energy saving achieved by using intermodal transport.

These values were calculated within the model. In formulating the model, the LF and EH
were added as adjustment factors to reflect the effect of freight loading on energy
consumption. Various input parameters in the optimisation model were determinedwith the aid of a questionnaire survey and a literature-based survey for domestic freighttransport in Thailand. The model was applied to the freight transport network between
Bangkok and Hat Yai in Thailand. Compared with the actual modal share, a 25%
reduction in energy consumption was observed for the base case of the Pareto-optimal
solution (45% for trailer-only, 11% for intermodal-rail and 44% for intermodal-waterway
transport). It is also revealed that the transport time has a trade-off relationshipwith energy consumption, and thus, the transport time should be increased
to reduce energy consumption. These results are consistent with the finding of previous
studies (e.g., Shinke et al., 2007) that a greater modal share of intermodal-rail andintermodal-waterway contributes more energy savings than using trailer-only.
This study shows eight Pareto-optimal solutions, which would be helpful forpolicy-makers to determine how much modal share of trailer transport should be shifted336

S. Hanaoka et al.

to intermodal-rail and intermodal-waterway transport to achieve higher energy savings.
For example, if the objective of policy in terms of energy savings is to consume less than
108 BTU, a policy-maker should aim to achieve a modal share corresponding
to alternative 6, 7, or 8 in Table 3. Additionally, the total transport time and the shipment
charge can be observed simultaneously. Such a model can help logistics companies
and government organisations/agencies to take effective policy decisions to promoteintermodal transport for sustainable environment.
As a future work, it is essential to understand the type of commodities that areappropriate for each transport mode. Commodities of higher Value of Time (VOT)
might be transported by trailer, which gives better transport times, particularly in
Thailand. Then, time constraints might be considered. In this study, detailed information
about the type of commodities transported by each mode was unavailable. Suchinformation is highly necessary for further discussion. In terms of capacity, if route 3(intermodal-waterway) cannot handle all the cargo volume between Bangkok and
Hat Yai, a 100% modal share of route 3 cannot be accomplished. Thus, the capacity
constraint should also be considered in the model.

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Notes
1

Exclusive line for freight transport connecting Laem Chabang and ICD has been operating since1996 by State Railway of Thailand (SRT). Cargo volume transported in this section by rail is morethan by trailer in 1997 and 1998, however, cargo volume of trailer caught up with of that by rail
in 1999. After that, cargo volume by trailer is continuously increased and finally, cargo volume
in 2008 became rail: 25% and trailer 75%, respectively. One of the reasons not to grow cargovolume by rail is due to single-truck problem. SRT is currently considering realising double-truck
train in this section (The data were collected in the interview survey to SRT in January 2009).
2
WWW-NIMBUS can display up to 15 alternative solutions from basic solution to minimum
energy consumption.
3
Proportionally distributed after excluding air transport: trailer: 94.98%, inland waterway: 4.7%
and rail: 0.3%.