U. R. TuzkayaInt. J. Environ. Sci. Tech., 6 (2), 277-290, Spring 2009ISSN: 1735-1472© IRSEN, CEERS, IAU

Received 10 August 2008; revised 17 November 2008; accepted 13 January 2009; available online 1 March 2009

*Corresponding Author Email: tuzkaya@yildiz.edu.tr Tel.: +90 212 383 2873

Evaluating the environmental effects of transportation modes using an integrated methodology and an application

*U. R. Tuzkaya

Department of Industrial Engineering, Mechanical Faculty, Yildiz Technical University,

34349 Istanbul, Turkey

ABSTRACT: The increasing needs for transportation of freight and passengers causes environmental impacts.Preventing studies for these impacts should be considered by logistics firms and encouraged by the regulations ofauthorities. An important contribution can be provided by determining the environmental effects of the transportationmodes in specific regions and using the most convenient ones. In this study, multiple criteria decision-making techniques,including human judgments, tangible and intangible criteria and priorities are used. Fuzzy analytic hierarchy processand preference ranking organization method for enrichment evaluation, as two of the multi criteria decision-makingmethodologies, are integrated for ranking the transportation modes in terms of the environmental effects of them. Theproposed decision-making process is applied to chose the environmentally convenient transportation mode withrespect to the determined evaluation criteria in Marmara Region of Turkey. The results indicate that, the main problemfor the Marmara Region about transportation aspects is to pass from the intensive utilization of the road transportationmode to another one. In this study it is seen that the most convenient transformation mode in Marmara Region is thesea transportation mode.

Keywords: Decision-making techniques, transportation modes, multi criteria decision-making, fuzzy approach

INTRODUCTIONTransportation systems can increase the

productivity and quality of life at the same time if theyare planned and managed properly. Although the needsof people stimulate the demand for transportation,environmental pollution and health care which are alsovery important for the people. The transportationsystems should ensure efficient movements ofpassengers and freights but, such a system shouldnot deplete the natural resources and badly affects theenvironment. Although the cost of transportation ismostly chosen as the main evaluation criterion fortransportation mode selection, many criteria should beconsidered to prefer a transportation mode to another.Today, the transportation systems are getting morecomplex structures in all around the world. Therefore,as the decision makers transfer big investment budgetsto shape the future of transportation systems, theyshould analyze many criteria beside the creterion ofcost.

Roles of transportation modes in transportationsystem decisions are very important. There are some

countries in Europe that restrict the usage of specifictransportation modes considering their environmentaleffects. For example, in some countries, transit passesof the trucks are permitted only on a rail flat car orsome transportation projects which use multimodalsystems encouraged by the governments or unions.One of the main reasons of imposing people to use orto integrate specific transportation modes is todecrease the various environmental effects. Whiletransportation systems expand and become moreintegrated, their impacts on the physical environment(air, water and land resources) will become morecomplex (Rondinelli and Berry, 2000).

Measuring the environmental effects oftransportation modes may be a complex processbecause of the different criteria which approach to thesubject from different aspects. Under certainconditions, determining the effects of transportationmodes on environment may seem more explicit.However, the criteria that contain uncertainties orcannot be given precisely are usually expressed inlinguistic terms by decision makers. This makes fuzzylogic a more natural approach to these kinds of

U. R. Tuzkaya

278

problems. Because of the difficulty of obtaining certaindata, such as non-normalized fuzzy ratings and fuzzyweights, fuzzy-logic based multi criteria decision-making (MCDM) methods may be efficiently appliedto the problem of determining the effects oftransportation modes on environment. Moreover, thefuzzy analytic hierarchy process (Fuzzy-AHP)methodology can be used to cope with uncertainhuman judgments. When the environmental criteria areconsidered, most of them contain linguistic terms andimprecise judgments. Therefore, the fuzzy-AHPmethodology is used to determine the environmentalcriteria weights. There is not a similar study in literaturethat applies fuzzy-AHP methodology on transportationprojects considering with environmental effects.Following the fuzzy-AHP step, another MCDMtechnique preference ranking organization method forenrichment evaluation (PROMETHEE) is chosen torank the alternative transportation modes because ofits easiness comparing with the other MCDMtechniques. Integration of these two methodologiesfor decision-making in environmental issues makes thisstudy unique. This integration completes eachtechnique by overcoming the obstacles of separatelyusing of them.

The characteristics of the transportation modes andthe environmentally evaluation criteria are discusseddetailed in section 2. Literature reviews and theexplanations of the materials and methods are given insection 3. In section 4, the application of the integratedmethodology is presented and the results and somescenarios analyses are discussed. This research hasbeen done in Yildiz Technical University, in Istanbul,Turkey, during the period of January-July 2008.

Transportation modes and environmental impactsSometimes, it may not be possible to use one

transportation mode instead of the other because ofthe geographical, infrastructural, freight type, etc.reasons. However, the advantages and disadvantagesof transportation modes should be considered fortransportation projects. The characteristics of thetransportation modes are summarized below (Rondinelliand Berry, 2000; Tuzkaya and Önüt, 2008).

Rail transport is the most commonly used mode forheavy and bulky loads over long land hauls (in general,greater than or equal to 200 km) without payingenormous charges. Trains can provide reasonably highspeed and they are conveniently linked with other

modes of transportation. Some of the advantages areconstancy, low-cost guarantee, greater reliability andthey are not affected by the weather and trafficconditions. The main disadvantages are inflexibility andspecified routes between fixed terminals; moreover,they do not stop at intermediate points. From theenvironmental aspect on railway, there are somedamages caused from improper fueling; maintenanceand cleaning rail car, locomotives and parts; oil andcoolant releases. However, using electricity power inrailway instead of the fuel or oil derivatives decreasesthese undesirable environmental effects.

For door-to-door transportation, the most widelyused mode is road transportation. Its main benefits arethe flexibility and the ability to reach rugged terrains.The ease of freight loading/unloading, lack ofnecessity for rigid timetables and the existence ofwidespread transit roads are advantageous. Thedisadvantages are high maintenance and fuel expenses,weight limitations and duties in transit countries. Also,fuel combustion, motor oil, brake and transmissionfluids, coolants, solvents, etc. pollute the air, soil andwater.

Rail and road modes are limited to land use; however,an important part of international trade is carried outby sea transport. Sea transportation can be classifiedinto three basic types: river and canals, coastalshipping and ocean transport. The main advantage ofsea transportation is the ability to transport largeamounts of bulk freights, liquids and containerizedfreights by ships and vessels. In addition, it is thecheapest transportation mode and there are no duty ortransit-passing transactions between the starting andarrival points; however, the damage risk is high, transittimes are long and there is a limitation and inflexibilitywith regard to finding appropriate ports. Bilge pumpingwaste disposal, tank cleaning, fueling are also harmfulfor water.

Air transportation is the most convenient modewhen slow speed is unacceptable. Loading andunloading operations can be carried out frequently andthe flexibility level can be increased. However, aircraftoperations create noise, engine emissions and wastedisposal problems.

In addition this study focuses on the environmentaleffects. According to the conditions, the importanceof the environmental effects shows differences.Because of this situation, specific criteria are chosenby considering related literature (Goldman and Gorham,

U. R. TuzkayaInt. J. Environ. Sci. Tech., 6 (2), 277-290, Spring 2009

279

2006; Qureshi and Huapu, 2007; Shiftan et al., 2003)and the necessities of the application region. Theexplanations of the nine evaluation criteria used in thisstudy are given below.

NoiseNoise is one of the most obvious and often-

mentioned negative impact of transportation traffic.Obviously, noise impacts vary by vehicle type,condition, location and time. For example, in road mode,tire-road noise is an important part of the vehicle noise.Different road surfaces may also give a large variationin noise levels. In addition, the engine of vehicle or thewrongly loaded freight can cause undesirable noises.As a result, comparing the noise impact of thetransportation mode, the number of the people affectedby noise is important. A comparison can be realizedamong the transportation modes since the noise costsare higher in urban areas and there are more humanears.

Emission reduction potentialEmissions of greenhouse gases are related with the

risk of generating environmentally undesirable gasesduring the transportations. Main effects of theemissions are shown on human health, climate change,and ozone damage. Social cost of a transportation modeconsists of two major components. First one is capitaland operating costs paid by users and all other coststhat result from the use of the transportation mode,but, which are not paid for directly by users (Delucchiet al., 1996). Emission values are considered in secondone and combustion of the each energy resource typehas different carbon, sulfur and nitrogen emissions.Aiming the reduction of emission values intransportation enforces to utilize specifictransportation modes.

Effects on open land and wild lifeTransportation modes cause different effects on

open land and wild life. For example, created noise,produced emissions, risks of accidents pollute the openlands and wildlife areas. Moreover, the greenhouseeffect from the combustion of the fuel in transportationvehicle causes global warming which is destructive foropen land and wildlife.

Undesirable viewIn general, transportation vehicles do not have good

views. There is a risk of bad looking that can be

constituted according to the freight or the container.The transportation modes and their networks whichpass through the urban sites increase undesirableview risks.

SafetyThis criterion may be explained as the ratio of

transportations concluding in an accident to alltransportations in a determined time period. Thepossibi l ity of the accidents and the rela tedenvironmental effects shows differences accordingto the transportation modes.

Energy resource utilizationThis criterion is related with the amount of energy

resource consumption per unit freight. The fuelconsumption of a motor vehicle is a function of anumber of characteristics of the vehicle and the trip:the size of the engine, the weight of the vehicle, theaerodynamic drag of the vehicle, the average speedof the trip, the number of stops and starts, theamount of time spent idling, etc. (Delucchi et al.,1996). The consumed energy type is also importantsince the specific modes are dependent on specificenergy resources. Especially, the fossil oil and itsderivatives are the limited resources. But at the sametime, they are the mostly used ones. Consideringwith these characteristic, tending to transportationmodes wh ich can use the al ternat ive andenvironmental fr iendly energy resources arepreferable.

Transportation capacity of the vehicleAs explained in the previous sections, different

vehicles are used in each transportation mode andtheir unit capacities are not the same. Transportationof a specific freight can required only one shipinstead of hundreds of trucks. Therefore, the capacityof the vehicle can change the environmental effectscomprehensively.

Infrastructure of the transportation networkThis criterion represents the infrastructure quality

of the routes between the points of departure andthe destinations. Asphalt quality and the lanenumbers of roads; signalization and rail quality ofra ilways; por t condi t ions and connectionpossibilities of sea and air ports may be listed as theinfrastructure quality of the transportation network.

U. R. Tuzkaya

280

Evaluating the environmental effects of transportation modes

Seasonal affectsTransportation modes are sensitive to seasonal

affects. For example, the maritime transportation isdependent on rigors of weather and its tardinesspossibility is greater than the railway transportation.In this instance, the criterion measures the responsesrate of transportation between demand and supplypoints in terms of the seasonal effects.

Literature review of the used MCDM, differentapplication areas and the studies integrating them aredetailed in this section. Then, the mathematicalexplanations of the methods are given.

Literature review of the used methodsMCDM is an important branch of decision-making

approaches. It deals with the decision problems underthe presence of a number of decision criteria. MCDMis divided into multi objective decision-making(MODM) and multi attribute decision-making (MADM)(Climaco, 1997). In MODM, there are not predeterminedalternatives for choosing one or ranking them. Insteadof including alternatives, MODM tries to optimize morethan one objective function subject to a set ofconstraints. At least, the obtained solution is the mostefficient one and it is not possible to improve theperformance of an objective function withoutdecreasing the performance of one other objectivefunction. However, in MADM, there is a set ofalternative solutions to be evaluated against a set ofattributes which are difficult to quantify (Pohekar andRamachandran, 2004).

Analytic hierarchy process (AHP) is one of themost frequently applied MADM methods for variousproblem types. The AHP provides an easy to useframework for the decision makers by giving theopportunity of assessing the weights of each criterionwith a nominal nine-point (1-9) scale. It is also availablefor fuzzy weights (Zimmermann, 1987). The AHP useshierarchic or network structures to represent adecision problem and then develops priorities for thealternatives based on the judgments of decision-makerthroughout the system (Saaty, 1980). The reason ofadopting AHP especia lly for the qual ita tiveperformance data is the fact that qualitative factorsare often complicated and conflict. In addition, theuser acceptability and confidence in the analysisprovided by the AHP methodology is high when it iscompared with other MADM methods (Zakarian andKusiak, 1999). Although the AHP has various benefits

and usefulness, several shortcomings have beenreported in the literature and some modifications aresuggested to deal with these shortcomings. The AHPtries to capture a decision maker’s knowledge but, ithas not the ability of fully reflecting the human thinkingstyle. In other words, the AHP approach is incapableof handling the inherent subjectivity and ambiguityassociated with the mapping of one’s perception to anexact number (Pan, 2008). Linguistic and vaguedescriptions could not be solved easily by the AHPunless by the development of fuzzy decision-making.Comparison of decision maker’s uncertain judgmentswith fuzzy logic can constitute more rational andaccurate results. Considering with this problems,Buckley (1985) developed a fuzzy-AHP model and afterthis study various developments of fuzzy-AHP methodsand applications have been carried out (Chang, 1996;Wang et al., 2007). Fuzzy version of the AHP wasapplied to various areas in the literature. Some of therecently published studies are given as follows. Huanget al. (2008) used fuzzy-AHP for selection ofgovernment sponsored research and developmentproject in Taiwan. Wang and Chin (2008) proposed amethod for fuzzy-AHP which utilizes a linear goalprogramming model to derive normalized fuzzy weightsfor fuzzy pairwise comparison matrices. A hybrid modelthat uses concepts from fuzzy logic and the AHP wasproposed for transportation route choice by Arslanand Khisty (2005). Also, a more general version of fuzzy-AHP, fuzzy analytic network process, was applied fortransportation mode selection considering eight mainand thirty two sub-criteria by Tuzkaya and Onut (2008).They applied the proposed approach for a logisticservice provider’s transportation project betweenTurkey and Germany. However, application of the fuzzy-AHP methodology on transportation projectsconsidering with the environmental effects cannot beseen in literature.

Weak preferences or incomparability are usuallyobserved in environmental decisions. Therefore, theusage of other MCDM methods can be requiredsometimes. For example, ELECTRE (the elimination andchoice translating reality) method has the capabilityof handling discrete quantitative and qualitative criteriain nature and provides complete ordering of thealternatives. Besides, this approach has shortcomingsthrough its complexity and nuances in the comparisons.To overcome ELECTRE’s obstacles, ranking methodPROMETHEE (preference ranking organization

U. R. Tuzkaya

281

Int. J. Environ. Sci. Tech., 6 (2), 277-290, Spring 2009

method for enrichment evaluation) has been developedb y B r a n s et al., 1986 and the flexibility and simplicityhas been brought together for the users. It can beadapted to problems where finite number alternativesare ranked considering weighted and sometimesconflicting specific criteria. Implementation of thismethod is constituted from seven steps which detailswill be given in the next section. First six steps of thecalculations give the partial precedence and named asPROHETHEE I. In the seventh step, the net precedenceof the alternatives is obtained for a complete rankingof all alternatives and named as PROMETHEE II.

This ranking method has been widely used forvarious application areas in the literature. Meanwhile,some of the recent studies include fuzzy numbersbecause of the difficulty of defining the data within areasonable degree of accuracy. In one of the recentstudy, PROHETHEE was used in the first step of thedecision-making process related to evaluating theoutsourcers (Araz et al., 2007). Pohekar andRamachandran (2004) prepared a review study relatedto the application of PROMETHEE and other MCDMmethods on sustainable energy planning. Anotherapplication was in iron and steel making industry foroutranking the environmental assessment (Geldermannet al., 2000). Again an extended version ofPROMETHEE was used in this study to handle thefuzzy data on preferences, scores and weights. UsingPROMETHEE, suitable ecotechnology method forevaluating was realized in Taiwan by Chou et al. (2007).Indices of ecotechnology methods were linked withconstruction sites and a practical construction caselocated in Shihmen reservoir watershed was chosenfor evaluation and verification.

There are some studies such as Macharis et al.(2004) in determining operational synergies in multicriteria analysis, De Brucker et al. (2004) in evaluationof intelligent transportation systems, Wang and Yang(2007) in information systems outsourcing, etc. thathandle these two MCDM methods together in differentareas. However, this study is unique for usingintegration of fuzzy-AHP and PROMETHEE on anenvironmental issue.

MATERIALS AND METHODSTwo step methodology: Fuzzy-AHP and PROMETHEE

The proposed methodology is developed with theAHP and PROMETHEE frameworks. The first step ofthe methodology is using the AHP technique with fuzzy

logic. The weights of the criteria, which are used inPROMETHEE for ranking the transportation modes,are gained by fuzzy-AHP calculations. In the secondstep, another multi criteria method, PROMETHEE, isapplied for the evaluation and ranking the alternativetransportation modes.

Fuzzy-AHP methodologyFuzzy set theory was introduced by Zadeh (1965)

to deal with vague, imprecise and uncertain problems.This theory has been used as a modeling tool forcomplex systems that are hard to define precisely, butcan be controlled and operated by humans (Dweiri,1999). More detailed discussions related to fuzzy sets,fuzzy relations and fuzzy operations can be found inRoss (2004).

Embedding the AHP method into fuzzy sets, anotherapplication area of fuzzy logic is revealed. Decisionmakers usually find that it is more confident to giveinterval judgments than fixed value judgment. This isbecause usually it is unable to explicit about thepreferences due to the fuzzy nature of the comparisonprocess (Kahraman et al., 2004).

In this study, Chang (1996) extent analysis methodis preferred, since the steps of this approach arerelatively easier than the other fuzzy-AHP approachesand similar to the crisp AHP. The steps of Chang’sextent analysis approach, by integrating theimprovements of Zhu et al. (1999), are as follows. LetX ={x1,x2,…,xn} be an object set and U = {u1,u2,…,um}be a goal set. Each object is taken and extent analysisfor each goal, gi, is performed, respectively. Therefore,m extent analysis values for each object can beobtained, with the following signs:

mggg iii

MMM ,...,, 21 i = 1,2, ... , n (1)

Where, all the jgi

M (j=1,2,...,m) are triangular fuzzy

numbers (TFNs).The steps of Chang extent analysis can be given as

follows:Step 1: The value of fuzzy synthetic extent with

respect to the ith object is defined as:

(2)

1

1 11

−

∑=

∑=

⊗∑=

= ⎥⎦⎤

⎢⎣⎡ n

i

m

jj

igMm

jj

igiS

U. R. Tuzkaya

282

⎟⎟⎠

⎞⎜⎜⎝

⎛∑=

∑=

∑=

=∑=

m

j jum

j jmm

j jlm

jjigM

1,

1,

11(3)

∑=

⎟⎠

⎞⎜⎝

⎛∑=

∑=

∑=

=∑=

n

i

n

i iun

i imn

i ilm

jjigM

1 1,

1,

11

(4)

and then, the inverse of the vector in Eq 4 is computedin the following order:

⎟⎟⎠

⎞⎜⎜⎝

⎛⎥⎦⎤

⎢⎣⎡

∑ =∑ =∑ ==

−∑=∑= n

i ilni imn

i iu

n

i

m

jjigM

1

1,

1

1,

1

11

1 1

( ) ( ) ( ) ⎥⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛

≥=≥ yMxMxy

MMV2

,1

m insup12 µµ (6)

(5)

and can be equivalently expressed as follows:

( ) ( ) ( )

( ) ( )⎪⎪⎪

⎩

⎪⎪⎪

⎨

⎧

−−−

−≥≥

==∩=≥

,1122

21

,21,0,12,1

22112

otherwiselmum

ululifmmif

dMMMhgtMMV µ

(7)

Step 2: The degree of possibility of( ) ( )11112222 ,,,, umlMumlM =≥= is defined as

and to obtain [ ] 11 1

−= =∑ ∑n

imj

jgi

M , the fuzzy adition

operation of jgi

M (j=1,2,…,m) values can be performedas follows:

Where, d is the ordinate of the highest intersection

point between 1Mµ and

2Mµ to compare M1 and M2

and both the values of ( )21 MMV ≥ and

( )12 MMV ≥ are needed.

( )( ) ( ) ( )[ ]

( ) .,...,3,2,1,min

...21

...,2,1

kiiMMVkMMandandMMandMMV

kMMMMV

=≥=

≥≥≥

=≥

(8)

Assume that

( ) ( ).min kSiSViAd ≥=′ (9)

For; k = 1, 2, ..., n and k ≠ i then, the weight vectoris given byW′ = (d′ (A1), d′ (A2), ..., d′ (An))T, (10)

Where, Ai (i=1,2,…,n) are n elements.Step 4: The normalized weight vectors via normalizationare:W = (d(A1), d(A2), ..., d(An))

T, (11)

Where, W is a nonfuzzy number.

PROMETHEE methodologyThe explanation and mathematically calculation

steps of the PROMETHEE are summarized below andmore information can be found in Araz et al. (2007) andGeldermann et al. (2000).

Let i represents the alternatives (i=1,2,…,m andAi∈ ), j represents the set of criteria (j=1,2,…,n andCj∈ ) and )(ijg is the value of criterion j of alternative

i. After the )(ijg values are determined in the first step,

preference function ( ) ( ) ( ) jxijgijgiijF =′−=′, , which isthe preference degree of alternative i in comparison to

i′ in terms of criterion j, is defined with one of the sixdifferent generalized shapes given in Fig. 1 with Eq.12-17 (Brans et al., 1986). The names of generalizedcriteria functions are usual criterion, quasi criterion,criterion with linear preference, level criterion, criterionwith linear preference and indifference area andGaussian criterion. q and p are the indifference andstrict preference thresholds of a specific criterion,respectively.

( )⎩⎨⎧

>≤

=0,10,0

xx

xp (12)

( )⎩⎨⎧

>≤

=qxqx

xp,1,0

(13)

( )⎪⎩

⎪⎨

⎧

≥<<

≤=

pxpxqpx

qxxp

,1,/,0

(14)

To obtain∑ =

m

jj

giM

1 , the fuzzy addition operation ofm extent analysis value for a particular matrix shouldbe performed as:

Step 3: The possibile degree for a convex fuzzynumber to be greater than k convex fuzzy numbers

U. R. TuzkayaInt. J. Environ. Sci. Tech., 6 (2), 277-290, Spring 2009

283

Fig. 1: Generalized preference functions

( )⎪⎩

⎪⎨

⎧

≥<<

≤=

pxpxqy

qxxp

,1,,0

(15)

( )⎪⎩

⎪⎨

⎧

≥<<−−

≤=

pxpxqqpqx

qxxp

,1,)()(,0

(16)

( )⎪⎪⎩

⎪⎪⎨

⎧

≥<<−−

≤

=px

pxqzxe

qx

xp,1

,222

1

,0

(17)

Then, the aggregated preference functions arecalculated for each alternative pair using the preferencefunctions obtained in the previous step. The next stepis calculating the preference index ( )ii ′,π with Eq 18which is a weighted average of preference functions( )jxP for all the criteria. Here, wj is the weight assigned

to criterion j and it is obtained from the fuzzy-AHPevaluations.

( )( )

∑=

∑=

=′ n

j jw

n

j jxPjwii

1

1,π (18)

Sum of the ( )ii ′,π is used as a measure of thestrength of the alternative i∈A and is named as leaving

( ) ( ) Aim

iii

iim

i ∈∀∑

≠′=′

′−

=+

1,

11 πφ

(19)

Another measure for the weakness of the alternativei∈A is entering flow. This is the outranking characterof alternative i as given in Eq. (20).

( ) ( ) Aim

iii

iim

i ∈∀∑

≠′=′

′−

=−

1,

11 πφ (20)

By the above calculations, required results areobtained for PROMETHEE-I and PROMETHEE-II. ThePROMETHEE-I partially preorders the alternatives bycomparing the leaving and entering flows anddetermines the weak preferences and incomparabilityof alternatives. When one of the Eqs. 21-23 areprovided, alternative i is superior to i′ .

flow. Thus, leaving flow yields a measure of theoutranking character of i as given in Eq. (19).

( ) ( ) ( ) ( )iiandii ′−<−′+>+ φφφφ (21)

( ) ( ) ( ) ( )iiandii ′−=−′+>+ φφφφ (22)

( ) ( ) ( ) ( )iiandii ′−<−′+=+ φφφφ (23)

If the Eq. 24 is realized, alternative i and i′ have thesame preferences.

( ) ( ) ( ) ( )iiandii ′−=−′+=+ φφφφ (24)

P (x)

1

P (x)

1

P (x)

1

P (x)

1

P (x)

1

P (x)

1

y

q = 0 q q p

q = 0 pq pq p x

x x

x

x

x

U. R. TuzkayaEvaluating the environmental effects of transportation modes

284

At least, alternative i and i′ are incomparable, whenone of the Eqs 25-26 is provided.

( ) ( ) ( ) ( )iiandii ′−>−′+>+ φφφφ

( ) ( ) ( ) ( )iiandii ′−<−′+<+ φφφφ

(25)

(26)

Even the partial preorders derived by PROMETHEE-I contain realistic information, complete preorders arerequested generally. This is yielded by calculating thenet flows as the difference of leaving and entering flowswhich is called as PROMETHEE-II given in Eq. 27:

( ) ( ) ( ).iiinet −−+= φφφ (27)

RESULTS AND DISCUSSIONThe application

The proposed methodology selects the mostconvenient transportation mode considering the effectsof them on environmental issues. The study is focusedon Marmara Region in Turkey. Geographical positionof Marmara Region allows to use all of the transportationmodes for national, international and transittransportations. Furthermore, there are manytransshipment points for transit passes of freights andpassengers in Marmara Region. This situation alsoincreases the importance of multimodal transportationwhich includes using at least two transportation modesin a journey. This is important because of reducing thebad affects of specific modes by decreasing the rate ofusing them in transportation.

There are eleven cities in Marmara Region. Istanbulis the most crowded city in Turkey and it is also thecenter of many industries, trades and cultures. Manyof the remaining cities in Marmara Region such asKocaeli, Bursa, Yalova, Adapazari and Tekirdag aremore important cities in terms of the per capita income(Fig. 2). As a result, the traffic levels of thetransportation modes are very high in this region. Trans-European Motorway, which is the motorway networkconstituted by the participation of many Europeancountries, passes from the Marmara Region. Being amember of this network increases the roadtransportation intensity very much. Besides, Trans-European Railway which passes from Marmara Regionis the same formation in railway network and increasesthe railway traffic. In addition, there are many largeand small size sea ports in the region which of them are

mostly placed in Istanbul and then in Kocaeli, Balikesirand Tekirdag. The container, dry-bulk and liquid freightcapacities of the ports in the region increaseconsistently. There are also 7 main airports generallyin the centers of these cities. When the intensities arecompared in terms of the constituted traffic of thetransportation modes, it is seen that the roadtransportation is the first one. However, manytransshipment points can be used to transfer the freightfrom road to sea or railway transportation modes.Therefore, multimodal transportation alternative isadded considering the effects of it to the environment.In Fig. 2, the railways are denoted with blue lines searoutes are denoted with red lines and the main airportsare specified with red circles. In the first step of thestudy, the criteria explained previously are selected bya decision-making group. This group is constitutedfrom different areas, i. e. academicians, government,municipality, environmental associations and logisticsfirms. Then, the group decides on the convenienttransportation modes to evaluation process. Road,railway, sea, air and multimodal transportation modesare chosen because of the possibility of using them inMarmara Region of Turkey for national and internationaltransportation and transit passes. Fig. 3 shows thestructure of the evaluation criteria and alternatives.Firstly, the precedence of the criteria in terms of thegoal of determining the most environmentalisttransportation mode is calculated by fuzzy-AHP.

First step calculations: Fuzzy-AHPAs mentioned in the previous sections, fuzzy-AHP

is used for determining the weights of the criteria to beused in PROMETHEE calculation steps. Before startingthe pair-wise comparisons of the criteria, six linguisticterms and their corresponding triangular fuzzy numbersare decided with the decision makers. Correspondenceof the linguistic terms “absolute, very strong, fairlystrong, strong, weak, and equal” can be given as (5,6,7),(4,5,6), (3,4,5), (2,3,4), (1,2,3), (1,1,1), respectively. Thefirst step is the preparing the comparison matrix of usedcriteria by decision-making group. As shown in Table1, the comparisons of the criteria according to the maingoal are realized by fuzzy triangular numbers. Then,using the Eq. 2, the values of fuzzy synthetic extentwith respect to the each criterion are calculated asgiven below:

SC1 = (5.75, 8.53, 12.75) Ä (1/91.57, 1/131.40, 1/176.40)= (0.033, 0.065, 0.139)

U. R. TuzkayaInt. J. Environ. Sci. Tech., 6 (2), 277-290, Spring 2009

285

Fig. 2: Map of the Marmara Region

Fig. 3: Hierarchical structure of the criteria and alternatives

Determining the most environmentalist transportation mode

C1 C2 C3 C4 C5 C6 C7C8 C9

Road (A1) Railway (A2) Sea (A3) Air (A4) Multimodal (A5)

Noi

se

Emis

sion

red

uctio

n po

tent

ial

Effe

cts

on o

pen

land

and

wild

life

Und

esira

ble

view

Safe

ty

Ener

gy r

esou

rce

utili

zatio

n

Tran

spor

tatio

n ca

paci

ty o

fth

e ve

hicl

e

Infra

stru

ctur

e of

the

trans

porta

tion

netw

ork

Seas

onal

affe

cts

Edirne Kirklareli

Tekirdag

AegeanSea

Cankkale

Balikesir

Istanbul

Black Sea

Busra

KocaeliSakarya

Bilecik

CentralAnatolia

MarmaraBlack sea

Eastern AnatoliaAegean

Mediterranean

SoutheastAnatolia

Tpiuvo Edirne

MankoTaphobo

Kirklareli

Luleburgaz

Uzunkopru

MalkaraKesan

Tekirdag

CorluCerkezkoy

SilivriEsenyurt

IstanbulGebze Adapazari

Ferizli

Karasu

Yalova GolcukKorfez

Eregli

Zonguldak

DuzceHendek

Akyazi

Bolu

Seben

BeypazNallihan

Sivrihisar

AlpuEskisehir

Mihaigazi

SeyitgaziKutahya

Bozuyuk

Emet

Tavsanli

Inegol

Keles

Yenicekoy

KestelBusraKaracabey

Bandirma

Susurluk

Balikesir

Soma

Gonen

Biga

Can

EdremitBurhaniye

Ayvalik

Ezine

Canakkale

EceabatLapsekiGelibolu

Scale: 1:23500000Scale: 1:9000000

Scale: 1:3300000

Black Sea

U. R. Tuzkaya

286

SC2 = (20.00, 28.00, 36.00) Ä (1/91.57, 1/131.40, 1/176.40) = (0.113, 0.213, 0.393)

SC3 = (8.92, 14.33, 20.50) Ä (1/91.57, 1/131.40, 1/176.40) = (0.051, 0.109, 0.224)

SC4 = (2.83, 3.45, 4.95) Ä (1/91.57, 1/131.40, 1/176.40)= (0.016, 0.026, 0.054)

SC5 = (7.50, 11.17, 15.00) Ä (1/91.57, 1/131.40, 1/176.40) = (0.043, 0.085, 0.164)

SC6 = (21.25, 28.33, 35.50) Ä (1/91.57, 1/131.40, 1/176.40) = (0.120, 0.216, 0.388)

SC7 = (5.84, 7.58, 10.53) Ä (1/91.57, 1/131.40, 1/176.40)= (0.033, 0.058, 0.115)

SC8 = (13.50, 19.67, 26.00) Ä (1/91.57, 1/131.40, 1/176.40) = (0.077, 0.150, 0.284)

SC9 = (5.98, 10.33, 15.17) Ä (1/91.57, 1/131.40, 1/176.40) = (0.034, 0.079, 0.166)

At the last step, the V values are calculated usingthe Eq. 7. Thus, the weight vector of the criteria areobtained from Eq.10 as Wc = (0.029, 0.260, 0.130, 0,0.066, 0.263, 0, 0.187, 0.065)T.

The weight vector of the criteria, Wc, shows that theweights of the forth and the seventh criteria are zeroand do not have any effect on the alternativetransportation mode selection. Moreover, the secondand sixth criteria have bigger effects on the selectionprocess.

Second step calculations: PROMETHEEThe PROMETHEE method is applied for evaluating

the transportation modes in terms of environmentaleffects of them. The weights of criteria, preferencefunction types of them and related parameter valueswhich are determined by the decision group is given inTable 2.

Some of the values of alternative transportationmodes are easy to obtain quantitatively such as Noisyin dB (VTPI, 2006) and and emission values in CO2

Table 1: Matrix of criteria comparisonsC1 C2 C3 C4 C5 C6 C7 C8 C9 (1,1,1) (0.33,0.5,1) (0.33,0.5,1) (2,3,4) (0.25,0.33,0.5) (0.17,0.2,0.25) (1,2,3) (0.33,0.5,1) (0.33,0.5,1) (1,2,3) (1,1,1) (1,2,3) (4,5,6) (1,2,3) (2,3,4) (5,6,7) (2,3,4) (3,4,5) (1,2,3) (0.33,0.5,1) (1,1,1) (3,4,5) (1,2,3) (0.33,0.5,1) (1,2,3) (0.25,0.33,0.5) (1,2,3)

25,0.33,0.5) (0.17,0.2,0.25) (0.2,0.25,0.33) (1,1,1) (0.33,0.5,1) (0.14,0.17,0.2) (0.2,0.25,0.33) (0.2,0.25,0.33) (0.33,0.5,1) (2,3,3) (0.33,0.5,1) (0.33,0.5,1) (1,2,3) (1,1,1) (0.25,0.33,0.5) (2,3,4) (0.25,0.33,0.5) (0.33,0.5,1) (4,5,6) (0.25,0.33,0.5) (1,2,3) (5,6,7) (2,3,4) (1,1,1) (3,4,5) (2,3,4) (3,4,5)

(0.33,0.5,1) (0.14,0.17,0.2) (0.33,0.5,1) (3,4,5) (0.25,0.33,0.5) (0.2,0.25,0.33) (1,1,1) (0.25,0.33,0.5) (0.33,0.5,1) (1,2,3) (0.25,0.33,0.5) (2,3,4) (3,4,5) (2,3,4) (0.25,0.33,0.5) (2,3,4) (1,1,1) (2,3,4) (1,2,3) (0.20,0.25,0.3) (0.33,0.5,1) (1,2,3) (1,2,3) (0.2,0.25,0.33) (1,2,3) (0.25,0.33,0.5) (1,1,1)

g/ton.km (Den Boer et al., 2008). However, determin-ing the values of alternatives in terms of the remain-ing criteria quantitatively is more difficult. Therefore,their values are determined in a brainstorming deci-sion-making process by the decision-making group.The compromised qualitative values of the alterna-tives according to the criteria are given in Table 3.

After choosing the evaluation criteria and thealternatives and determining the convenientpreference function types and related parametervalues for each criterion, preference function valuescan be calculated. As an example, A1 (road) and A2(railway) alternative pair is considered. For the noisecriterion (C1), A1 is better than A2. Because of the lowervalue is better, the absolute value of the difference isconsidered. Thus, the calculations are realized asfollows:

f(A1) – f(A2)= |80 – 100| = 20 and P1(A1,A2) = 20 / (30-0) = 0.667.

The similar calculations are realized for theundesirable view criterion (C4) as shown below:f(A1) – f(A2) = |2 – 4| = 2 and P4(A1,A2) = 2 / (4 – 0) = 0.5.

For the remaining criteria, alternative A1 has worsevalues than the A2. Therefore, the preference functionvalues of P2(A1,A2), P3(A1,A2), P5(A1,A2), P6(A1,A2),P7(A1,A2), P8(A1,A2), P9(A1,A2) are equal to 0.

Then, the preference index can be constituted forA1 and A2 alternative pair as given in Eq. 28:

( )

0194.01

000005.0*000667.0*0.0292,1

=

++++++++

=AAπ (28)

Same calculations are realized to obtain thepreference indexes of each alternative pair as given inTable 4.

U. R. Tuzkaya

A1 A2 A3 A4 A5 A1 0.0194 0.0326 0.2459 0.0058A2 0.4383 0.0839 0.2919 0.1055A3 0.5287 0.1611 0.2920 0.1983A4 0.6428 0.3012 0.2469 0.3098A5 0.3976 0.0783 0.0939 0.2734

 

Int. J. Environ. Sci. Tech., 6 (2), 277-290, Spring 2009

287

Table 2: Summary of the used data in PROMETHEE

C1 C2 C3 C4 C5 C6 C7 C8 C9 Weights 0.029 0.260 0.130 0 0.066 0.263 0 0.187 0.065Preference Function Type V Shape V Shape V Shape V Shape V Shape V Shape V Shape V Shape V Shape

Max/Min Min min min min max min max min maxIndifference (Q) 0 0 0 0 0 0 0 0 0Preference (P) -30 -100 -8 -4 7 -20 8 -15 10 

Table 3: The obtained values of the alternative transportation modes for each criterion

Road Railway Sea Air Multimodal Noise (dB) 80 100 70 110 0 Emission reduction potential (CO2 g/ton km) 114 42 31 2 57

Effects on open land and wildlife 10 8 6 2 4

Undesirable view 2 4 5 1 3.5 Safety 5 8 6 12 7 Energy resource utilization 7 4.5 1 22 5.95

Transportation capacity of the vehicle 1 8 10 6 6.3

Infrastructure of the transportation network 20 10 8 5 12

Seasonal affects 15 20 10 12 18  

( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( )

5019.015

3976.06428.05287.04383.015

1,51,41,31,21

0759.015

0058.02459.00326.00194.015

5,14,13,12,11

=−

+++=

−

+++=−

=−

+++=

−

+++=+

AAAAAAAAA

AAAAAAAAA

ππππφ

ππππφ

(29)

Table 4: Calculated preference indexes for alternativetransporta tion modes

Table 5: Leaving and entering flows for alternativetransporta tion modes

A1 A2 A3 A4 A5

( )i+φ 0.0759 0.2299 0.2950 0.3752 0.2108

( )i−φ 0.5019 0.1400 0.1143 0.2758 0.1549

Using the obtained preference indices in Table 4,leaving and entering flows can be calculated. An ex-ample of leaving and entering flows calculation is givenfor A1 in Eq. 29.

These calculations are repeated for the otheralternatives and results of them are summarized inTable 5.

PROMETHEE I determines the partial precedenceusing ( )i+φ and ( )i−φ values in Eqs (21-26). As shownin Fig. 4, A3 outranks A2 and A2 outranks A5. A1 isoutranked by all other alternatives. However, it is notpossible to compare A3, A2 and A5 with A4. Partialprecedence of the alternatives is not sufficient todetermine the best alternative.

When the complete precedence is obtained byPROMETHEE II, it is seen that the A3 is the best one andA4 is placed between the A3 and A2. The complete ranksof the alternative A3, A4, A2, A5 and A1 are 0.181, 0.099,0.090, 0.056, -0.426, respectively.

RESULTS AND DISCUSSIONThe complete the ranking with PROMETHEE II shows

that sea transportation mode is the best alternative, whenthe entire criteria are considered. The precedence values

U. R. TuzkayaEvaluating the environmental effects of transportation modes

288

A3    A2     A5          

 +φ  

0.295    +φ  0.2299    +φ   0.2108         

 −φ   0.1143     

−φ 0.14     −φ 0.1549         

                           A1 

                            +φ   0.0759

                            −φ   0.5019

                  A4          

                   +φ 0.3752         

                   −φ 0.2758         

 Fig. 4: Partial ranking with PROMETHEE I

of air and railway transportation modes are close to eachother. Multimodal transportation alternative takes all theadvantages and disadvantages at the same time.Therefore, it is better than the road alternative, but takesonly the fourth place in the ranking. The roadtransportation is the worst environmental affectingalternative. The numerical values of net flows also showthat the gap between road transportation mode and theothers are very high. This is a critical result because theworst chosen alternative is the mostly used one inMarmara Region. However, to realize a deeper analysisand to validate the obtained results, some scenarios wereconsidered. The results were reached according to theobtained criteria weights with fuzzy-AHP. Even the fuzzy-AHP methodology was used and the uncertainties weretaken into account for determining the criteria weights,evaluating the rank of alternatives in terms of the changesin criteria weights is important. This is also required forjustifying the model. The obtained results of the differentscenarios are proved that there is not an inconsistency inthe model and it is working as expected. The weights ofthe criteria were changed in four different scenarios. Thefirst scenario neglects the criteria weights. In other words,the equal weights were assumed for PROMETHEEcalculations. This scenario showed that the net flowvalues of alternatives were changed, but only the ranksof first alternative (sea) and the second alternative (air)were affected Fig. 5. Besides, it can be mentioned that thenormal results showed the preferences more explicitly,but the equal weights brought the net flow values closer.

Second scenario changes the weights of the criteriawhich are ranked in first two (C2 and C6) and last two (C4and C7) places. This scenario is also important forvalidating the model to show that changing the weightsoppositely changes the preferences oppositely too. Theresults were obtained in the expected manner and larger

decreases in sea and railway alternatives and increases inair alternative were observed. This means that decreasingin “the emission reduction potential” and “energyresource utilization” criteria removes the attractivenessof the sea and railway alternatives. Besides, airtransportation is better in undesirable view andtransportation capacity of the vehicle.

The third scenario increases the intensities of first twoplaced criteria, C2 and C6. The aim of this scenario is toreach the marginal points towards the inclination of thedecision-making group. 50 % precedence was given toeach two C2 and C6 and the remaining criteria wereneglected by giving 0 % weights. This situation supportedthe original results by increasing the magnitudes of theprecedence of the alternatives. Thus, precedence of therail and sea alternatives were reached to top points andprecedence of the air alternative was failed to the forthplaces with a very low net flow value.

The last scenario is the reverse of the third scenarioand 50 % precedence was given to each two C4 and C7,which have zero weight in the original solution and theremaining criteria were neglected by giving 0 % weights.This scenario was also realized to validate the proposedmethodology from the opposite side. The results werevery similar to the second scenario results, but theywere more striking because of sharing out the total weightto only C4 and C7. The road transportation modealternative was ranked as the last alternative for theoriginal solution and for each scenario. Since thisalternative took the worst values for most of the criteria,changing the criteria weights did not affect deeply thenet flow value of the road transportation alternative.The preference of the multimodal transportation did notchange deeply in any scenario. Since the multimodaltransportation includes all transportation modes, it isgenerally balanced in criteria weight changes. Its place

U. R. Tuzkaya

Fig. 5: Ranking of the alternatives according to the scenario analysis

was changed between second and forth places. Afterthe overall analysis, it is seen that even though the roadalternative is preferred intensively in Marmara Regionin real life; sea, air and railway transportation alternativesshould be taken into account in terms of theenvironmental effects. Air transportation is ranked asthe second alternative in original solution, but its rankdecreases when the weights of the higher value takencriteria are increased. In addition, transporting manyfreight types is not possible by air transportation mode.All these indications focus on the usage of sea or railwaytransportation modes instead of road transportationmode. In the east-west line in Marmara, use of railwayand sea modes are convenient. For example, the line runalong from Tekirdag to Izmit provides to decrease roadintensity in Bosphorus bridges in Istanbul and relatedenvironmental effects. Also, the railway alternative canbe used in that line, but there is a disconnection again inBosphorus and passing that point with ferryboat takeover the multimodal transportation alternative. In thenorth-south line in Marmara again, the sea transportationis practicable instead of going around the Marmara Seaby road transportation mode. However, it is not verysuitable to use railway in that line because of the lack ofinfrastructure investments.

As a result, the main problem is to pass from road toan alternative mode. As it is seen, the most convenientmode alternative in Marmara is the sea transportationmode. There are many crowded cities in Marmara andmany other environmentally bad effecting points suchas heavy industries, disposal collection points, wastes

poured out to sea, etc. Minimizing the environmentaleffects caused by transportation such as, noisy,emissions, energy usage, etc. will provide a big benefitsfor people, sea, air and open lands of the region.

CONCLUSIONThis study represents the environmental effects of

alternative transportation modes and a decision-makingprocess to rank them considering the environmentalcriter ia. Nine criter ia are determined by anenvironmentalist group to choose one among the fivealternative transportation modes in Marmara Regionof Turkey. Two MCDM method is integrated by adecision making group to obtain a realistic and usableresult.

The main contribution of the study is to combinemany environmental criteria and to synthesizing themin a two step integrated solution approach. Applyingthis approach in a specific region, consideringdynamics and specifications of this region and obtainingvalid and reasonable results are other important points.The evaluation of the decision-making group was alsoexamined with different scenarios and the consistencyof the obtained results was proved.

In future studies, different MCDM methods may betried to combine for determining the similarities anddifferences between the obtained results and decisionprocesses. As another direction, this study may beconsidered as a pilot application to extend this analysisto entire country with more criteria or sub-criteria fordetermining strategic transportation policies.

289

Int. J. Environ. Sci. Tech., 6 (2), 277-290, Spring 2009

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

A1 A2 A3 A4 A5

Transportation mode alternatives

Results of application Scenario 1 Scenario 2

Scenario 3 Scenario 4

φ

U. R. Tuzkaya

REFERENCESAraz, C.; Ozfirat, P. M.; Ozkarahan, I., (2007). An integrated

multicriteria decision-making methodology for outsourcingmanagement. Compu. Oper. Res., 34 (12), 3738-3756(19 pages).

Arslan, T.; Khisty, C. J., (2005). A rational reasoning methodfrom fuzzy perceptions in route choice. Fuzzy Set. Syst., 150(3), 419-435 (17 pages).

Brans, J. P.; Vincke, B. H.; Mareschal, B., (1986). How to selectand how to rank projects: The PROMETHEE method. Eur.J. Oper. Res., 24 (2), 228-238 (11 pages).

Buckley, J. J., (1985). Fuzzy hierarchy analysis. Fuzzy Set. Syst.,17 (3), 233-247 (15 pages).

Chang, D. Y., (1996). Applications of the extent analysis methodon fuzzy AHP. Eur. J. Oper. Res., 95 (3), 649–655 (7 pages).

Chou, W. C.; Lin, W. T.; Lin, C. Y., (2007). Application offuzzy theory and PROMETHEE technique to evaluate suitableecotechnology method: A case study in Shihmen ReservoirWatershed, Taiwan. Ecol. Eng., 31 (4), 269-280 (12 pages).

Climaco, J. (1997). Multicriteria analysis. New York: Springer-Verlag.

De Brucker, K.; Verbeke, A.; Macharis, C., (2004). Theapplicability of multicriteria-analysis to the evaluation ofintelligent transport systems (ITS). Res. Transport. Econ., 8(1), 151-179 (21 pages).

Delucchi, M. A.; Wang, M. Q.; Ceerla, R., (1996). Emissions ofcriteria pollutants, toxic air pollutants and Greenhouse gases,from the use of alternative transportation modes and fuels.Institute of Transportation Studies, University of California,Davis, Research Report UCD-ITS-RR-96-12.

den Boer, L. C.; Brouwer, F. P. E.; van Essen, H. P., (2008).STREAM, Studie naar transport emissions van allemodaliteiten. Rapport VERSIE 1.0, CE Delft, Oplossingenvoor milieu, economie en technologie.

Dweiri, F., (1999). Fuzzy development of crisp activityrelationship charts for facilities layout. Comput. Ind. Eng.,36 (1), 1-16 (16 pages).

Geldermann, J.; Spengler, T.; Rentz, O., (2000). Fuzzyoutranking for environmental assessment, Case study: Ironand steel making industry. Fuzzy Set. Syst., 115 (1), 45-65(21 pages).

Goldman, T.; Gorham, R., (2006). Sustainable urban transport:Four innovative directions. Tech. Soc., 28, 261-273 (13).

Huang, C. C.; Chu, P. Y; Chiang Y. H., (2008). A fuzzy AHPapplication in government-sponsored R&D project selection.Omega, 36 (6), 1038-1052 (15 pages).

Kahraman, C.; Cebeci, U.; Ruan, D., (2004). Multi-attributecomparison of catering service companies using fuzzy AHP:The case of Turkey. Int. J. Prod. Econ., 87 (2), 171-184(14 pages).

Macharis, C.; Springael, J.; De Bruckerc, K.; Verbeke, A., (2004).PROMETHEE and AHP: The design of operational synergiesin multi criteria analysis. Strengthening PROMETHEE withideas of AHP. Eur. J. Oper. Res., 153 (2), 307-317 (11 pages).

Pan, N. F., (2008). Fuzzy AHP approach for selecting the suitablebridge construction method, Automation in Construction. 17(8), 958-965 (8 pages).

Pohekar, S. D.; Ramachandran, M., (2004). Application ofmulti-criteria decision making tosustainable energy planning- A review. Renew. Sust. Energ. Rev., 8, 365-381 (17 pages).

Qureshi, I. A.; Huapu, L., (2007). Urban transport and sustainabletransport strategies: A case study of Karachi, Pakistan.Tsinghua Sci. Tech., 12 (3), 309-317 (9 pages).

Rondinelli, D.; Berry, M., (2000). Multimodal transportation,logistics and the environment: Managing interactions in aglobal economy. Eur. Manag. J., 18 (4), 398-410 (13 pages).

Ross, T. J., (2004). Fuzzy Logic with Engineering Applications,2nd. (Ed.), John Wiley and Sons Ltd.

Saaty, T. L., (1980). The analytic hierarchy process. RWSPublications, Pittsburgh, PA.

Shiftan, Y.; Kaplan, S.; Hakkert, S., (2003). Scenario building asa tool for planning a sustainable transportation system.Transport. Res. D-Tr. E., 8 (5), 323–342 (20 pages).

Tuzkaya, U. R.; Önüt, S., (2008). A fuzzy analytic networkprocess based approach to transportation- mode selectionbetween Turkey and Germany: A case study. Inform. Sci.,178 (15), 3132-3145 (14 pages).

VTPI, (2006). Transportation Cost and Benefit Analysis – NoiseCost, Victoria Transport Policy Institute, Guide Book, Canada.

Wang, J. J.; Yang, D. L., (2007). Using a hybrid multi-criteriadecision aid method for information systems outsourcing.Comput. Oper. Res., 34 (12), 3691-3700 (10 pages).

Wang, L.; Chu, J.; Wu, J., (2007). Selection of optimummaintenance strategies based on a fuzzy analytic hierarchyprocess. Int. J. Prod. Econ., 107 (1), 151-163 (13 pages).

Wang, Y. M.; Chin, K. S., (2008). A linear goal programmingpriority method for fuzzy analytic hierarchy process and itsapplications in new product screening. Int. J. Approx. Reason.49 (2), 451-465 (15 pages).

Zadeh, L. A., (1965). Fuzzy sets. Inform. Control, 8 (3), 338-353 (18 pages).

Zakarian, A.; Kusiak, A., (1999). Forming teams: An analyticalapproach. IIE Trans., 31 (1), 85-97 (13 pages).

Zhu, K.; Jing, Y.; Chang, D., (1999). A discussion on ExtentAnalysis method and applications of fuzzy AHP. Eur. J. Oper.Res., 116 (2), 450-456 (6 pages).

Zimmermann, H. J., (1987). Fuzzy sets, Decision-making andexpert systems, Kluwer, Boston.

AUTHOR (S) BIOSKETCHESTuzkaya, U. R., Assistant professor, Department of Industrial Engineering, Mechanical Faculty, Yildiz Technical University, 34349 Istanbul,Turkey. Email: tuzkaya@yildiz.edu.tr

This article should be referenced as follows:Tuzkaya, U. R., (2009). Evaluating the environmental effects of transportation modes using an integrated methodology and an application.Int. J. Environ. Sci. Tech., 6 (2), 277-290.

290