Transportation (2005) 32: 603–626 DOI: 10.1007/s11116-005-0645-x
Springer 2005
Incorporating uncertain and incomplete subjective judgments into the evaluation procedure of transportation demand management alternatives PARINYA TANADTANG1, DONGJOO PARK2,* & SHINYA HANAOKA3 1
Office of Transport and Traffic Policy and Planning (OTP), Ministry of Transport, Building No. 35, Petchaburi Road, Phayatai, Ratchatevee, Bangkok 10400, Thailand; 2Department of Transportation Engineering, University of Seoul, 90 Junnong, Dongdaemoon, Seoul, Korea; 3 School of Civil Engineering, Asian Institute of Technology, PO Box 4, Klong Luang, Pathumthani 12120, Thailand (*Author for correspondence, E-mail:
[email protected])
Key words: multi-criteria decision making, subjectivity, transportation demand management, uncertainty Abstract. This paper proposes a methodology for evaluating transportation demand management (TDM) alternatives in the context of multi-criteria decision making (MCDM). The proposed approach takes into account not only quantitative criteria (i.e. transportation and environmental impacts) but also qualitative criteria (i.e. social impacts) which are intrinsically uncertain and subjective. The transportation impacts of the TDM alternatives are estimated by TRIPS1 program, while the MOBILE5a2 is employed in order to estimate environmental impacts in terms of NOX, CO and Hydrocarbon. The social impacts of the TDM alternatives are estimated by interviewing relevant experts. Consequently, the uncertain subjective judgements were quantified by the evidential reasoning (ER) approach based on decision theory and Dempster–Shafer theory of evidence. In order to measure the weights of criteria, analytical hierarchy process (AHP) is adopted. As a last step, the CODASID3 method based on a complete concordance and discordance analysis is used to rank alternative TDM schemes. The proposed approach is demonstrated by ranking 14 TDM alternatives, which are chosen for the central business area in Bangkok, Thailand of 22-square-kilometers.
1. Introduction It has been realized that continuing expansion of transportation facilities is not always economically or environmentally feasible. As a result, the present major breakthrough for improving transportation system quality is instead consisting of management strategies that result in more efficient use of the existing transportation resources. In this context, transportation demand management (TDM) has become important. It can help address a variety of problems and provide a variety of economic, social and envi-
604 ronmental benefits. Moreover, it is flexible and can help achieve equity objectives, and support sustainable economic development. The TDM strategies are often considered as the most cost-effective way to improve the efficiency of the transportation systems. Evaluating TDM alternatives for reducing traffic congestion calls for careful and articulate procedures. Since transportation demand is a derived demand, the evaluation of the TDM alternative(s) should include not only its effects on transportation system performance but also its effects on other socioeconomic activities including the externalities such as adverse environmental effects. In addition, other criteria, particularly, social aspects such as equity and easiness of its implementations should be included as well. The existing evaluation methods of the TDM alternatives may be classified as two classes: the monetary and non-monetary approaches. The first category includes cost effectiveness, benefits cost analysis, life cycle cost and leased cost fixed analysis. The monetary approach may be sub-classified based on the number of criteria: a single and multiple criteria monetary approach. The single criterion method of the monetary approach usually takes into account the vehicle kilometers of travel and converts them as monetary value (Dagang 1993; Rutherford et al. 1994; Beaton et al. 1995; Taylor et al. 1997). The multiple criteria monetary approach includes various aspects such as air pollution, energy consumption, and consumer welfare and then converts them into the comparative monetary terms (Euritt et al. 1994; Rodier & Johnston 1997; Litman 2002). The non-monetary approach usually incorporates both quantitative and qualitative criteria. This approach is further sub-classified into two categories: (i) the single criterion method (e.g. congestion reduction) where the output is presented in term of scores ranking (Beroldo 1990; Higgins 1991; Black et al. 1992; Zupan 1992), and (ii) the multiple criteria method which includes congestion reduction, air quality, energy conservation, and accessibility (Frietz et al. 1980; Donavan & Ulberg 1994; Mekky 1998). The monetary approach has intrinsic limitations in setting parameters such as unit costs. On the other hand, the existing non-monetary approaches are limited in that they have only considered qualitative criteria which are relatively easy to measure without systematically incorporating social, economic and environmental factors (Black et al. 1992; Donavan & Ulberg 1994). In particular, the social impacts of the TDM schemes such as equity impacts (e.g. fairness and public subsidies) and action impacts (e.g. barriers to implementation and acceptability of various stakeholders) have been overlooked even if they are crucial to identify the most promising and practical alternative. It is considered that these social impacts have not been included in the decision making process because of the difficulty of their measurement.
605 Basically the measurement of the social impacts may be obtained by interviewing experts and therefore it would be uncertain or subjective. That is, the estimation of the social impacts of a TDM alternative may be imprecise and missing assessments due to the lack of data, shortcomings in expertise, time pressure and /or the willingness or capability on the decision maker to provide incomplete assessments. To evaluate the equity impacts of ‘‘contra-flow bus lanes’’, for example, an expert may only be able to state that she is (i) 80% sure that its fairness is excellent; (ii) Absolutely sure (100%) that its affordability is average; and (iii) 50% sure that the its public subsidies is indifferent and 50% sure that it is average. The percentage values of 50, 80 and 100 are the uncertain subjective judgments associated with the fairness, affordable and public subsidies of ‘‘contra-flow bus lanes’’ being evaluated to indifferent, average and excellent. The objective of this study is to apply a multi-criteria decision making (MCDM) approach for evaluating TDM alternatives by simultaneously considering transportation, environmental and social impacts. The social impacts of the TDM alternatives are estimated by uncertain subjective judgments by interviewing relevant experts. Consequently, the uncertain subjective judgments are quantified by the evidential reasoning (ER) approach based on decision theory and the Dempster–Shafer theory of evidence. Note that fuzzy sets theory (Avineri et al. 2000) and Interactive group decision making procedure (Kim & Ahn 1999) were suggested in order to incorporate uncertain subjective judgments of the decision makers in the context of MCDM. However, these methods are different from the ER-based MCDM approach in the procedures of eliciting, quantifying, and combining decision makers’ judgments. That is, the ER-based MCDM approach allows the decision makers to express not only their uncertain but also imprecise preferences. In addition, it relies on the evidential reasoningbased probability assignment and combination method in order to quantifying and combining decision makers’ uncertain and imprecise preferences (see Yang & Singh 1994 for details). In order to measure the weights of criteria, an analytical hierarchy process (AHP) is adopted. The CODASID method based on a complete concordance and discordance analysis is adopted to rank alternative TDM schemes or to select the best compromise design. The 14 TDM alternatives, which are chosen for the central business area in Bangkok of 22-square-kilometers, are ranked to demonstrate the proposed approach.
606 The remainder of this paper is organized into four sections. Section 2 presents the proposed approach while section 3 demonstrates the case study. Discussions and conclusions are presented in section 4.
Notation Index Variables k, j s r, i n t l C I
– – – – – – – –
criteria index, sub-criteria index, alternative index, evaluation grade index, number of quantitative criterion, number of qualitative criterion, number of combined factors, number of local probability assignments.
Evidential Reasoning-based Variables – kth quantitative or qualitative criterion, – rth TDM alternative (scheme), – numerical value of kth quantitative criterion of rth alternative, – numerical value of sth quantitative sub-criterion of kth yks quantitative criterion, – nth evaluation grade, Hn – factor influencing the evaluation of sth qualitative sub-criteesk rion of kth qualitative criterion, – nth set of factors esk , En – nth set of factors En, Sn – nth set of factors Sn, Rn h – frame of discernment, m(X) – basic probability assignment X, – local probability assignments partially combined from the Rn mIðRn Þn factors, m(Hn/En) – overall probability assignment to which the state of an criteriaat an alternative ar is confirmed to Hn, – renormalized factor of rth alternative of Cth combined facKC(r+1) tors, yk ar yrk
CðjÞ
e1;jþ1
CðjÞ
IðR Þ
IðR Þ
– combined factors in En ; e1;jþ1 ¼ e1;2 1 ; . . . ; ej;jþ1j ; n ¼ 1; . . . ; N
607 CðjÞ
bn
G Prk L wc ws p{Hn}
– combined factors of overall probability assignment, CðjÞ bCðjÞ ¼ mðHn =e1;jþ1 ; n ¼ 1; . . . ; N n – overall probability assignment vector, – preference degree of kth criterion of rth alternative, – confidence degree of subjective judgment of qualitative criterion, – weight for criteria, – weight for sub-criteria, – preference degree space of nth evaluation grade.
2. Evidential reasoning-based multiple criteria decision making (ER-based MCDM) There are many MCDM methods proposed in the literature (see Huang & Yoon 1981, for a classification of these methods), each having different ways of eliciting a DM’s assessments in order to evaluate alternatives based on multiple criteria. The (AHP) developed by Saaty, for example, asks DMs to compare alternatives in a pairwise fasion based on each decision criterion (Saaty & Wind 1980). However, the AHP method has been criticized by some academics because of: (i) the scale used, (ii) redundant information required from the DM, (iii) the occurrence of rank reversals and (iv) the comparison of two criteria represented by two totally different scales (Stewart 1992). Multiple attribute utility theory (MAUT) on the other hand, uses the concept of utility to determine a DM’s real preferences, judgements and attitudes towards risk (Keeney & Raiffa 1993). However, this approach also places a burden on DMs by asking a large number of hypothetical lottery-type questions in order to discover their real preferences. When facing MCDM problems, particularly group decision making situations with qualitative criteria, the following difficulties may be encountered: (i) different types of assessments (e.g., numbers, linguistic terms, and/or stochastic values) depending on the characteristics of the decision criteria, and (ii) imprecise and missing assessments due to the lack of data, shortcomings in expertise, time pressure and/or the decision maker’s inability or capacity to provide incomplete assessments (Kim & Ahn 1999). Subsequently, DMs may not give consistent answers to these questions. Like the AHP, MAUT requires DMs to provide exact numbers (i.e. probability values) so that their utility functions can be derived. Another disadvantage of MAUT is that the decision-making process takes a long time and becomes tedious if there are numerous criteria. As a part of the efforts to deal with MCDM problems with uncertainties, incomplete
608 assessments and subjectivity, the ER was developed and has been implemented for decades, and it has been increasingly applied to deal with these kinds of various MCDM problems. In addition, the ER theory was introduced into the theory of approximate reasoning which is based on a different paradigm for fuzzy reasoning. For example, probabilistic uncertainty in the output of a system can be included in the fuzzy model using the ER paradigm (Yager 1995). The ER approach was first introduced by Dempster in 1967 and further extended by his student Shafer in 1976. It is based on decision theory and the Dempster–Shafer theory (D–S theory) of evidence, and offers a rational and reproducible methodology to aggregate uncertain, incomplete, and vague data (Yang & Sen 1994; Yang 2001). The ER approach uses the concept of degree of belief to ascertain and make explicit a decision maker’s preferences. The degree of belief can be described as the degree of expectation an alternative will yield on an anticipated outcome of a particular criterion. An individual’s degree of belief depends on the knowledge of the subject and the experience. A detailed explanation is given in Yang and Sen (1994), Xu and Yang (2001), and Yang (2001)’s works. The ER decision making process is briefly described here in stepwise manner 1. Display a decision problem in a hierarchical structure, 2. Assign weights to each (main) problem criterion and also to their subcriteria (if any), 3. Choose a method for assessing a criterion either quantitatively or qualitatively, 4. Transform assessments between a main criterion and its associated subcriteria, if they are assessed using different methods (i.e., quantitative and qualitative), and 5. Quantify qualitative assessments at the top level if necessary and determine an aggregated value for each alternative. 2.1. Basic concepts and algorithms of evidential reasoning approach In the ER framework, belief degrees obtained from subjective judgements are an input to be converted into an output, preference degrees, by using Dampster’s rule of combination. The principal of the combination rule is described under the basic concept of ER from the frame of discernment, parameters and how to combine rules of combination. The frame of discernment, denoted by h, is a sample space in the D–S theory and is a finite nonempty set of propositions. A basic proposition is denoted by Hn i.e., Hn h. In h, all propositions are required to be mutually
609 exclusive and exhaustive. A probability mass function to every subset X of h X h can be assigned, denoted by mðXÞ. The basic probability assignment, a (BPA) function, is mðXÞ:2h![0,1] where such that mð/Þ ¼ 0 and X mðXÞ ¼ 1 ð1Þ Xh
where 0 mðXÞ 1, for all X h. The portion of the total belief is exactly (i.e. 100%) committed to hypothesis X, given a body of evidence is indicated by m(X). The quantity m(h) is a measure of the portion of the total belief that remains unassigned after the commitment of belief to all subsets of h. Ignorance or missing information in the D–S theory is m(h). If mðXÞ ¼ sðX hÞ and it is also known that no belief is assigned to other subsets of h, then mðhÞ ¼ 1 s. Hence, the remaining belief is assigned to h, not to the negation of the proposition X (i.e., not the complement of X). The D-S theory also includes reasoning based on its rule of combination, which is subsequently defined. Given two BPAs such that m1 ðXÞ and m2 ðXÞ, based on independent evidence, in h, then the next task is to obtain a combined BPA, denoted by m12 ðXÞ, which is calculated according to Dempster’s rule of combination as follows: m12 ð/Þ ¼ 0;
K¼
X
m12 ðXÞ ¼
X m1 ðAÞm2 ðBÞ 1K A\B¼X
ð2Þ
m1 ðAÞm2 ðBÞ
ð3Þ
A\B¼/
where m12 ðXÞ is computed from m1 and m2 by adding all products of the form m1 ðAÞm2 ðBÞ. Note that A and B are selected from the subsets of h in all possible ways, such that their intersection is X. K is the mass that the combination is assigned to a null subset. It represents the contradictory evidence (Murphy 2000). Dempster’s rule of combination in equations (2) and (3) can determine the overall combination algorithms. Table 1 (i.e. Intersection Table I) shows the sample of probabilities, the combination by Cð2Þ 1ðR Þ 1ðR Þ following the statement of e1;3 ¼ fe1;2 1 ; e2;3 2 g IðR1 Þ
Cð2Þ
¼ KCð2Þ m1
Cð2Þ
¼ KCð2Þ ðm2
Cð2Þ
¼ KCð2Þ mh
fH1 g : b1 fH2 g : b2 fH3 g : b3
IðR2 Þ
IðR1 Þ
IðR1 Þ
ð4aÞ
mh
IðR2 Þ
m2
IðR2 Þ
m3
IðR1 Þ
þ m2
IðR2 Þ
mh
IðR1 Þ
þ mh
IðR2 Þ
m2
Þ
ð4bÞ ð4cÞ
610 Cð2Þ
fhg : bh
IðR1 Þ
¼ KCð2Þ mh
IðR2 Þ
ð4dÞ
mh
where h i1 IðR Þ IðR Þ IðR Þ IðR Þ IðR Þ IðR Þ ð4eÞ KCð2Þ ¼ 1 m1 1 m2 2 þ m1 1 m3 2 þ m2 1 m3 2 n o Cð3Þ IðR Þ IðR Þ IðR Þ Then, in order to combine e1;4 ¼ e1;2 1 ; e2;3 2 ; e3;4 3 , Table 2 (i.e. Intersection Table II) is constructed. According to the combination rule, the following equations are obtained: Cð3Þ
¼ KCð3Þ b1
Cð3Þ
¼ KCð3Þ b2
fH1 g : b1 fH2 g : b2
Cð3Þ
fH3 g : b3
Cð3Þ
fH4 g : b4
Cð3Þ
fhg : bh
IðR3 Þ
ð5aÞ
IðR3 Þ
ð5bÞ
Cð2Þ
mh
Cð2Þ
mh
Cð2Þ IðR Þ Cð2Þ IðR Þ Cð2Þ IðR Þ ¼ KCð3Þ b3 m3 3 þ b3 mh 3 þ bh m3 3 Cð2Þ
¼ KCð3Þ bh
Cð2Þ
¼ KCð3Þ bh
IðR3 Þ
ð5cÞ
m4
ð5dÞ
IðR3 Þ
ð5eÞ
mh
where Cð2Þ
KCð3Þ ¼ ½1 ðb1
Cð2Þ
þ b2 Cð1Þ
Since b1
IðR3 Þ
m4
IðR1 Þ
¼ m1
Cð2Þ
þ b1
Cð2Þ
þ b3 Cð1Þ
; b2
IðR3 Þ
m4
IðR3 Þ
m4
IðR2 Þ
¼ m2
Cð2Þ
þ b2
IðR3 Þ
m3
Þ1
ð5fÞ Cð1Þ
; and bh
IðR1 Þ
¼ mh
Cðjþ1Þ
; then e1;jþ2 ¼
Þ
IðR
IðR Þ
IðR3 Þ
m3
jþ1 g is combined in a similar way and the following recursive fe1;2 1 ; ; ejþ1;jþ2 algorithm is obtained:
IðRjþ1 Þ
ð6aÞ
CðjÞ
IðRjþ1 Þ
ð6bÞ
¼ K Cðjþ1Þ b1 mh
Cðjþ1Þ
¼ K Cðjþ1Þ bj
fHj g : bj
Cðjþ1Þ
fHjþ1 g:bjþ1
Cðjþ1Þ
mh
CðjÞ IðR Þ CðjÞ IðR Þ CðjÞ IðR Þ ¼KCðjþ1Þ bjþ1 mjþ1jþ1 þbjþ1 mh jþ1 þbh mjþ1jþ1
Cðjþ1Þ
fHjþ2 g : bjþ2 fhg : bh
CðjÞ
Cðjþ1Þ
fH1 g : b1
CðjÞ
IðR
¼ K Cðjþ1Þ bh mjþ2jþ1 CðjÞ
IðRjþ1 Þ
¼ K Cðjþ1Þ bh mh
Þ
ð6cÞ ð6dÞ ð6eÞ
611 Table 1. Intersection Table I. IðR Þ
e2;3 2
Cð2Þ
e1;3
IðR Þ fH2 g m2 2
IðR Þ fH3 g m3 2
IðR Þ fhg mh 2
IðR Þ fH1 g m1 1
IðR Þ IðR Þ f/g m1 1 m2 2
IðR Þ IðR Þ f/g m1 1 m3 2
IðR Þ IðR Þ fH1 g m1 1 mh 2
IðR Þ fH2 g m2 1
IðR Þ IðR Þ fH2 g m2 1 m2 2
IðR Þ IðR Þ f/g m2 1 m3 2
IðR Þ IðR Þ fH2 g m2 1 mh 2
IðR Þ fhg mh 1
IðR Þ IðR Þ fH2 g mh 1 m2 2
IðR Þ IðR Þ fH3 g mh 1 m3 2
IðR Þ IðR Þ fhg mh 1 mh 2
IðR Þ
e1;2 1
Table 2. Intersection Table II. IðR Þ
e3;4 3
Cð3Þ
e1;4
IðR Þ fH3 g m3 3
IðR Þ fH4 g m4 3
IðR Þ fhg mh 3
Cð2Þ fH1 g b1
Cð2Þ IðR Þ f/g b1 m3 3
Cð2Þ IðR Þ f/g b1 m4 3
Cð2Þ IðR Þ fH1 g b1 mh 3
Cð2Þ fH2 g b2
Cð2Þ IðR Þ f/g b2 m3 3
Cð2Þ IðR Þ f/g b2 m4 3
Cð2Þ IðR Þ fH2 g b2 mh 3
Cð2Þ fH3 g b3
Cð2Þ IðR Þ fH3 g b3 m3 3
Cð2Þ IðR Þ f/g b3 m4 3
Cð2Þ IðR Þ fH3 g b3 mh 3
Cð2Þ fhg bh
Cð2Þ IðR Þ fH3 g bh m3 3
Cð2Þ IðR Þ fH4 g bh m4 3
Cð2Þ IðR Þ fhg bh mh 3
Cð2Þ
e1;3
where " KCðjþ1Þ ¼ 1
j X
CðjÞ bt
IðR Þ IðR Þ CðjÞ IðR Þ mjþ1jþ1 þ mjþ2jþ1 þ bjþ1 mjþ2jþ1
!#1 ð6fÞ
t¼1
When j = N – 2, the overall probability assignments are generated and can be expressed by the following vector, called the overall probability assignment vector:
612 CðN1Þ
G ¼ ½b1
CðN1Þ T
; ; bCðN1Þ ; ; bCðN1Þ ; bh n n
ð7Þ
The overall probability assignments are all zero for other hypotheses in h, except for the singletons Hn ðn ¼ 1; ; NÞ and h. So, the following equation should be true: N X
CðN1Þ
bCðN1Þ þ bh n
¼1
ð8Þ
n¼1 CðN1Þ
Since mðHn =En ðaÞÞ ¼ bHn equation (9). prk ¼
N X
, the preference degree, prk , is obtained by
mðHn =En ðar ÞÞpðHn Þ þ mðh=En ðar ÞÞpðhÞ
n¼1
¼
N X
ð9Þ CðN1Þ bHn pðHn Þ
þ
CðN1Þ bh pðhÞ
n¼1
2.2. Construction of an evaluation matrix After the preference degree of qualitative criterion is obtained, the ER decision making process transforms quantitative criteria into preference degrees. All of the preference degrees then are put into (a) extended decision matrix (matrices). The values of quantitative criteria, which are generally incommensurate, may also be transformed into the preference degree space using the following formulas (Yang & Sen 1994): Prk ¼ PðYrk Þ ¼
2ðyrk ymin k Þ 1; k ¼ 1; . . . ; t for benefit criteria max yk ymin k
ð10Þ
Prk ¼ PðYrk Þ ¼
2ðymax yrk Þ k 1; max yk ymin k
ð11Þ
k ¼ 1; . . . ; t for cost criteria
where ymax ¼ maxfy1k ; y2k ; . . . ; yrk g k
ð12Þ
¼ minfy1k ; y2k ; . . . ; yrk g ymin k
ð13Þ
The transformed criteria yk may be denoted by a preference function p(yk). Thus, an evaluation matrix is defined as Table 3, in which the states of all criteria, either quantitative or qualitative, are represented in the preference degree space.
613 2.3. Alternative ranking At this stage, several traditional MCDM methods can be used to rank alternatives on the basis of the evaluation matrix. The CODASID method is one of them, which has been developed, based on a complete concordance and discordance analysis for information aggregation. The reason that this study has chosen CODASID for alternative rankings is that it is appropriate to address an MCDM problem represented by the evaluation matrix obtained from the ER-Based approach. The CODASID algorithm can be found in Yang and Singh (1994), Yang and Sen (1994), and Tanadtang (2004). A sample problem of the application of the ER-based MCDM approach in the TDM evaluation is also demonstrated in Tanadtang (2004).
3. Case study: CBD area of Bangkok, Thailand 3.1. Problem definition: Target year 2003 Local Area Model version II (LAM II) is a model developed for describing the actual traffic conditions in the central business area of Bangkok, testing traffic management measures and evaluating junction and road network improvements (TDMC 2000). This study selected the area of LAM II as a test bed network. The LAM II area covers about 22 km2 with total 115 zones, encompassed with 66 internal and 49 external zones, as shown in Figure 1. This paper evaluated fourteen TDM alternatives that have been considered practical in Bangkok by the previous studies (Park 1989; Haider 1993; Sharma 1998; Limapornwanitch 2001) as shown in Table 4. These alternatives can be grouped into five categories: (i) traffic restriction strategy and traffic restraints, (ii) public transportation improvement, (iii) peak period dispersion strategy, (iv) ride sharing, and (v) parking control strategy. In this study, quantitative criteria are presented in terms of traffic and environmental impacts. Traffic impacts are evaluated by means of the Table 3. Evaluation matrix (preference degree). Alternatives
Quantitative Criteria (yk)
Qualitative Criteria (yk)
(a)
p(y1)
p(y2)
...
pðyt Þ
pðytþ1 Þ
pðytþ2 Þ
...
pðytþl Þ
a1 a2 ... ar
p11 p21 ... pr1
p12 p22 ... pr2
... ... ... ...
p1t p2t ... prt
p1tþ1 p2tþ1 ... prtþ1
p1tþ2 p2tþ2 ... prtþ2
... ... ... ...
p1tþl p2tþl ... prtþl
614 vehicle kilometers of traveled (VKT), vehicle hours of traveled (VHT), and speed. The environmental impacts are estimated by the emission rates of exhaust carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx). The social impact criterion includes two aspects: equity and action impacts. The equity impact consists of three sub-criterion: fairness, affordability, and public subsidies. The action impact includes three sub-criterion: best practices, barriers to implementation, and the acceptability of various stakeholders. The detail description of sub-criteria of social impacts is shown in Table 5. In this study, 42 experts from transportation, environment and operations fields were selected as decision makers. Table 6 shows the numbers of the experts selected from each group. They were interviewed to obtain (i) the weights for main criteria and sub-criteria and (ii) the subjective assessments of the social impacts of the proposed TDM alternatives. 3.2. Experimental study design At first, 42 experts were interviewed in order to decide the weights for main criterion and sub-criterion. In the second step, the performance of the 14 proposed TDM alternatives in terms of transportation, environmental and social aspects were evaluated. Consequently the states of these quantitative
Figure 1. LAM II network.
615
Table 4. Alternatives of transportation demand management (TDM). Alternatives 1. Limitation of Old Vehicles 2. Fuel Taxes Increase 3. With-flow Bus Lane
4. Contra-flow Bus Lane
5. Transit frequency increase 6. School bus service for students 7. Feeder system
8. Park & ride facilities 9. Staggered working hours
10. Flextime working hours
11. Car pooling
12. Staff bus service
13. HOV lane
14. Parking charges
Descriptions
Specific Area
• Set only 12 years lifespan of vehicle • Increases fuel tax (environmental taxes) by 25% • Reserves one lane for buses alone (same directions as the other vehicles) • Reserves one lane for buses alone (opposite directions as the other vehicles) • Increases frequencies of operation buses • Provides bus service for students
LAM II
• Provides bus service for passengers to make model shift from transit to mass rapid transit • Provides parking facilities at the transit terminals • Makes the beginning of working hours staggered in order to spread the peak-hour travel demand • Permits employees to have flexibility in distribution of working hours during a week • Offers three commuters share a single car for their commuting • Organizes the independence operation or commuter clubs to run shuttle buses and serve the commuters • Gives the priority to high occupancy vehicles, such as three occupants, which may require to be considered on HOV lane • Installs parking meters, which enables control of parking duration and generation of revenues
LAM II Sukhumvit road
Phetchburi road
LAM II School among Phetchburi road, Sukhumvit road, and Rama IV road Line 3 (Klongton-Ekkamai) and line 4 (Thong lo-Phrom Phong) Area near MRTA (RAMA IX) Government Agencies zone Bank zone Hotel zone
Government Agencies zone Bank zone Hotel zone
Government Agencies Zone Bank Zone Government Agencies zone Bank zone
Sukhumvit Road Phetchburi Road Government Agencies zone Bank zone LAM II
616 Table 5. Definition of factors and qualitative criteria of social impact. Criteria
Sub-criteria
Indicator
Equity impacts
Fairness
• How much do they treat everybody equally? • How much do the individuals bear the costs they impose (such as charging users directly for using the parking facilities)? • How much are they progressive with respect to income (assumes that public policies should benefit lower-income people)? • How much do they benefit transportation disadvantaged (such as improvement mobility and access for transportation disadvantaged groups, for example, non-drivers, people with disabilities)? • How much do they improve the basic mobility?
Affordable
Public subsidies
Action impacts
• How much do they cooperate with other TDM strategies? • How much do they encourage the competition and innovation in services? • How much do they provide the incentives to attract and retain users? Barriers to imple- • How much are they politically accepted? mentation • How much do they lack the effective plan, administration, operation and enforcement? • How much do they limit funds and support? • How much do they need to change the existing institutional practices? • How much do they accept the various stakeholders such Acceptability of as local or regional governments, law enforcement various agencies, individual businesses, neighborhood associastakeholders tions, individual residents, environmental organizations, transportation or transit agencies, private companies, employers, or user groups? Best practices
Sources: VTPI, 2003.
and qualitative criteria were presented in terms of preference degrees. In the third step, the ranking of proposed TDM alternatives was obtained by using CODASID method. 3.2.1. Obtaining weights of criteria and sub-criteria using AHP The experts’ opinions in each criteria and sub-criteria were taken through the pair wise comparison matrix of the questionnaire. For the analysis of each expert group and the overall group judgment, the consistent individual comparison matrices were used to develop the group comparison matrices through geometric means. Finally, the weights of the criteria and sub-criteria were obtained from the EXPERT CHOICE model, which was based on the AHP.
617 Table 6. Experts selected for questionnaires survey. Sector
Organization
Number of Samples
Traffic experts Environmental experts Operation experts
AIT, OCMLT, JMP, AEC AIT, PCD, CU, Team, TESCO RTP
14 14 14
Note: AIT: Asian Institute of Technology; OCMLT: Office of the Commission for the Management of Land Traffic; JMP: JMP (Thailand) Ltd.; AEC: Asian Engineering Consultant Ltd.; PCD: Pollution Control Department; CU: Chulalongkorn University; Team: Team Consultant Ltd.; TESCO: TESCO Consultants Ltd.; RTP: Royal Thai Police.
For a quick analysis of the decision problem with the AHP, it allows all input required by the AHP algorithm to formulate the problem in a hierarchical, pair wise comparison (verbal and numerical modes), and synthesize the results. The consistency ratio (C.R.) for each comparison for the overall problem was also determined in this step (Saaty & Wind 1980). 3.2.2. Impact estimations of the proposed TDM schemes 3.2.2.1. Traffic impact. The traffic impact of the proposed TDM alternatives were estimated by TRIPS, which is one of the transportation planning software packages used for transport demand modeling and network analysis. TRIPS comprises several modules, such as demand modeling, highway analysis, matrix estimation, public transport networks, and graphic and TRIPS Manager. Each module provides a particular modeling functionality (TRIPS 2003). Using the base year condition, the established travel demand model was tested as to whether it could reproduce the link flow of passenger cars. The criterion of convergence (d = 0.01) was satisfied after 316 times of iteration. The data for validation were derived from the traffic counts survey conducted by the Office of the Commission for the Management of Land Traffic (OCMLT). Percentage of root mean square error (%RMSE) was 95.8% (see Figure 2). Due to space limitation, the detailed description of the modeling assumptions used in order to estimate the traffic impacts of the proposed TDM alternatives is not provided in this paper (see Tanadtang 2004 for details). The results of the daily travel projections of the proposed TDM alternatives for the year 2003 scenario are shown in Table 7. It was found that the HOV lane alternative is the most effective alternative decreasing the VKT by 9.5%. The limitation of old vehicles schemes provided the highest reduction in the VHT of 25.4% at best. On the other hand, limitation of old vehicles scheme appeared to be the best option for the average speed travel improvement of 27.9%.
618 5000
Modelled Flows (PCUs)
4000
3000
2000
R2 = 0.9575
1000
0 0
1000
2000
3000
4000
5000
Observed Flows (PCUs)
Figure 2. Relationship between traffic volumes from model and survey during morning peak.
3.2.2.2. Environmental impact. In this study, an established MOBILE5aTHAI model developed by Pollution Control Department and Radian Corporation (Radian 1994) was used in order to estimate the relationships between travel speeds and emission factors. The calibrated emission factors for CO (gram per kilometer per vehicle) for various vehicle types and speed ranges of between 0 and 95 kph is shown in Figure 3. The obtained emission factor was multiplied by its speed in order to estimate the environmental impact of each TDM alternative. The estimated environmental impacts of the proposed TDM alternatives are shown in Table 7. It can be seen that the limitation of the old vehicles is the best option. 3.2.2.3. Social impact. Survey questionnaires were distributed to the 42 experts and they were asked to provide their judgments or confidence degrees of the sub-criteria of social impacts of the proposed TDM alternatives. Then, the confidence degrees were transformed into preference degrees using the ER approach. The results are shown in Table 7. As one can see in Table 7, the public transportation improvement group, such as transit frequency, increase, with-flow bus lanes, contra-flow bus lanes and school bus service for student were found to be the best. It is considered that these alternatives are favorable in terms of the equity issue by serving the needs of people with social, physical and/or transportation disadvantages, without creating action-related issues. On the other hand, the group of traffic constraint measures, such as limitation of old vehicles and increments in fuel tax, was ranked as the worst. This result is not surprising. For example, implementation of the fuel tax increase measure inherently evokes the political resistance from petroleum,
Performance/ preference egree
Dispersion
Peak Period
Improvement
Transportation
Public
Constraints
Traffic
Do-nothing limitation of old vehicles (a1) Increment in fuel tax (a2) With-flow bus lane (a3) Contra-flow bus lane (a4) Transit frequency increase (a5) School bus service (a6) Feeder system (a7) Park & ride facilities (a8) Staggered working hours (a9) Flextime working hours (a10) 2,049,150
414,937
2,275,753
415,069 2,113,630
2,316,777
421,678
416,826
2,285,581
2,225,634
395,871
420,113
2,225,937
398,624
1,963,582
2,376,256
421,886
412,003
2,404,397 1,794,038
424,001 424,542
12.1
11.8
10.9
10.9
11.0
12.6
10.7
10.7
10.7
10.6 13.5
41,614
42,850
45,558
46,351
45,818
39,902
44,290
44,379
47,264
47,734 36,202
(y 21) Kg
3,664
3,743
3,902
3,968
3,932
3,551
3,773
3,787
4,025
4,062 3,309
(y 22) Kg
593
600
602
611
609
589
574
578
612
615 574
(y 13) Kg
NOx
0.254 0.232 0.085
0.076
)0.028 )0.015
)0.009
0.166
0.158
0.176
0.225
0.171
0.098
0.256
0.193
0.234
0.077
(e14) –
0.139
(e33) –
Barriers to Implementation (e24) –
Action Impacts (y4) Best Practices
0.073
(e23) –
(e13) –
Public Subsidies
0.141
Affordable
Fairness
(y 12) (y 13) PCU-min km/hr
HC
(y11) pcu-km
Units
CO
Equity Impacts (y3)
Environmental Impacts (y2)
VHT
VKT
Speed
Traffic Impacts (y1)
Def. of Criteria
Def. of Sub-criteria Groups
Alternatives
Qualitative
Type of Critria
Quantitative
Table 7. Extended decision matrix for evaluation of TDM schemes.
Acceptability of Various Stakeholders (e34) –
619
Parking Control
Ride Sharing
Car pooling (a11) Staff Bus Service (a12) HOV Lane (a13) Parking Charges (a14) 1,986,794
2,102,634
383,869 411,800
2,271,907
2,283,324
412,964
413,337
12.4
11.0
10.9
10.9
40,365
42,107
45,426
45,633
(y 21) Kg
3,579
3,608
3,890
3,902
(y 22) Kg
589
557
599
599
(y 13) Kg
NOx
(e23) –
(e13) –
0.083 0.075
)0.116
0.132
)0.052
(e14) –
Barriers to Implementation (e24) –
Action Impacts (y4) Best Practices
0.221
(e33) –
Public Subsidies
0.089
)0.017
Affordable
Fairness
(y 12) (y 13) PCU-min km/hr
HC
(y11) pcu-km
Units
CO
Equity Impacts (y3)
Environmental Impacts (y2)
VHT
VKT
Speed
Traffic Impacts (y1)
Def. of Criteria
Def. of Sub-criteria Groups
Alternatives
Qualitative
Type of Critria
Quantitative
Table 7. Continued.
Acceptability of Various Stakeholders (e34) –
620
621 vehicle and transportation industries and motorists. In addition, implementation of old-vehicle limitation tends to be unacceptable from the perspectives of lower income people. 3.3. Analysis of results 3.3.1. Weights for criteria and sub-criteria The weights for traffic, environmental, equity, and action impacts from all experts was found as 0.337, 0.230, 0.172 and 0.261, respectively (C.R. = 0.01). It means that the traffic impact is the most important followed by the action impact, while the equity impact is the least important one. All three groups agreed that traffic impact is most important, even if the weights for traffic impact from each group are different. The operations experts gave the highest weight for traffic impact as high as 0.427, while other two experts group gave 0.306 and 0.303. It is worthwhile to note that the environmental experts gave relatively higher weight for environmental impact compared with the other expert groups (i.e. 0.289), while the traffic experts did for action impact (i.e. 0.292). Among the sub-criteria of the traffic impact, VHT was ranked as most important (w = 0.436), while the average speed in the middle (w = 0.387) and VKT at the lowest level (w = 0.177) (C.R. = 0.01). This pattern of ranking in the weights of sub-criteria of the traffic impact was observed from all three expert groups. For the sub-criteria of environmental impacts, weights of Carbon monoxide (CO), Hydrocarbons (HC) and Nitrogen oxides (NOx) obtained from the environmental experts group were about 0.485, 0.280 and 0.235, respectively (C.R. = 0.01). The relative importance of the sub-criteria of the environmental impacts from other groups are similar to that from the environmental experts. 3.3.2. Ranking of the Proposed TDM Alternatives Table 8 shows the final ranking and the relative closeness index of the proposed TDM alternatives under different criterion. When traffic and environmental impacts were taken into account (i.e. conventional TDM evaluation approaches), limitation of old vehicles was ranked as the best followed by transit frequency increase and parking charges. On the other hand, when only the social impact was considered, the feeder system was chosen as the best option while the limitation of old vehicles was the worst. That is, the relative rankings of the proposed TDM alternatives with different criterion gave totally different results. It can be seen that, if all three criteria were taken into account, the transit frequency increase scheme was found to be the best option, followed by the with-flow bus lane. The fuel tax increase alternative was
622 Table 8. Ranking of the proposed TDM schemes. Rank
Ranking based on Traffic & environmental impacts
Social impacts (equity and action)
Overall impacts (relative closeness index)
Feeder system
Transit frequency increase With-flow bus lane
(1.000)
3
Limitation of old vehicles Transit frequency increase Parking charges
(0.773)
4
HOV lane
Limitation of old vehicles Feeder system
5 6
Staff bus service Contra-flow bus lane
(0.704) (0.704)
7
Flextime working hours Staggered working hours Contra-flow bus lane
Park & ride facilities
(0.597)
8 9
With-flow bus lane Staff bus service
Parking charges HOV lane
(0.531) (0.526)
10
Park & ride facilities
(0.517)
11
Car pooling
12
School bus service for student Feeder system Fuel tax increase
Flextime working hours School bus service for student Staggered working hours Car pooling Fuel tax increase
1 2
13 14
With-flow bus lane Contra-flow bus lane Transit frequency increase Staff bus service Park & ride facilities School bus service for student Car pooling Staggered working hours Flextime working hours HOV lane Fuel tax increase Parking charges Limitation of old vehicles
(0.890)
(0.711)
(0.402) (0.387) (0.267) (0.000)
ranked as the poorest, while the parking charge alternative was ranked as the ‘‘eighth’’. It is interesting to note that the relative closeness index of the second best option (i.e. with-flow bus lanes) is only about 90% of that of the best option (i.e. transit frequency increases). The role of the social impact can be found by the fact that among 14 alternatives, the final ranks of the 13 alternatives are different from those based on only traffic and environment impacts.
4. Conclusion This paper proposed a methodology for evaluating TDM alternatives in the context of MCDM. The proposed approach took into account not only quantitative criterion (i.e. transportation and environmental impacts) but also qualitative criterion (i.e. social impacts), in which both quanti-
623 tative and qualitative information was represented in a unified manner through equivalent knowledge transformation. The emphasis of the study was to apply an evidential reasoning approach to estimate the social impacts of the TDM alternatives from the experts in a more practical and rational manner. The subjective judgments of experts for the social aspects of TDM schemes were assumed to be uncertain, imprecise, incomplete, and/or expressed by linguistic variables. It should be noted that in the current ER modeling framework it is required that evaluation grades be mutually exclusive and collectively exhaustive. However, the two evaluation grades ‘‘good’’ and ‘‘very good’’ may not be completely divided. Research is being conducted to develop ER algorithms for aggregating assessments based on non-exclusive evaluation grades. Another drawback of ER approach is that it requires complicated calculations that are difficult to complete by hand. Central business district (CBD) area of Bangkok, Thailand was selected as a study area for the application of the proposed methodology. Fourteen TDM alternatives were evaluated and the ranking of them was estimated. The transit frequency increase alternative was ranked as the best while the fuel tax increase as the worst. It was found that the social impacts have a significant influence on the ranking of the TDM alternatives. In this paper, all groups of experts were equally treated. It is recommended that sensitivity analysis on the different weighting of the various experts’ groups may provide more valuable results. In addition further studies may include sensitivity analysis of different quantitative values of the traffic impacts and the environmental impacts. Acknowledgements The authors are grateful to the Office of the Transport and Traffic Policy and Planning (OTP) and the Transportation Development Model Center (TDMC) for their contributions in the data collection and model calibration. This study is a part of a dissertation for a doctoral degree in the Asian Institute of Technology (AIT) under the financial supported of the Royal Thai Government (RTG).
Notes 1. Transport Improvement Planning System (TRIPS) is a transportation planning package which enables strategic as well as detailed analyses of multi-modal transportation networks.
624 TRIPS provides a framework for implementing a wide range of travel demand forecasting models. In Thailand, the TRIPS packages are used with the Transportation Model of Thailand (developed by OCMLT and the Transportation Development Model Center (TDMC)) in order to predict the traffic conditions for the CBD of Bangkok. 2. MOBILE5 is a computer program that estimated hydrocarbon (HC), carbon monoxide (CO), and oxides of nitrogen (NOx) emission factors for gasoline fueled and diesel highway motor vehicles. It was developed by EPA of US government. More detail information is available at www.epa.gov. This study employed the MOBILE5a-THAI model developed by the Bangkok Pollution Control Department in 1994 to assess the emission factors of the studied pollutants in Bangkok Area (1993–2010). 3. The Concordance and Discordance Analyses by Similarity to Ideal Designs (CODASID) method has been developed by integrating the favorable features of the TOPSIS method and ELECTRE method (Huang & Yong 1981).
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626 About the authors Parinya Tanadtang is a doctoral student of school of civil engineering at the Asian Institute of Technology in Thailand. His research area covers travel demand modeling, transportation demand management and transportation-related environmental modeling. Dongjoo Park is an associate professor of department of civil and environment engineering in Kongju National University (KNU), Korea. Before joining KNU, he worked for the Asian Institute of Technology in Thailand. His research interests include transportation demand modeling and intelligent transportation systems. He obtained his doctoral degree from Texas A&M University in the USA. Shinya Hanaoka received a Ph.D. in information sciences from Tohoku University, Japan. After obtaining his doctoral degree, he worked as a researcher at the Institute for Transport Policy Studies at Tokyo for four years. He is currently an Assistant Professor at the School of Civil Engineering at the Asian Institute of Technology in Thailand.
