MULTI-CRITERIA DECISION MAKING FOR PUBLIC TRANSPORTATION DEVELOPMENT PROJECTS USING ANALYTIC NETWORK PROCESS (ANP)

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1 MULTI-CRITERIA DECISION MAKING FOR PUBLIC TRANSPORTATION DEVELOPMENT PROJECTS USING ANALYTIC NETWORK PROCESS (ANP) MINTESNOT Gebeyehu Ph.D. student Graduate School of Engineering Hokkaido University North 13, West 8, Kita-Ku, Sapporo, , Japan Fax: Shin- ei TAKANO Associate Professor Graduate School of Engineering Hokkaido University North 13, West 8, Kita-Ku, Sapporo, , Japan Fax: Abstract: In Asian and African developing cities, decisions on transportation projects are made with capital cost constraints and administrative influences, thus, providing a well-designed public transport is not a simple task. Therefore, multi-criteria decision-making methods that can incorporate the conflicting considerations are essential. This case study introduced the application of ANP for public transportation development programs. Even though ANP is the generalization of AHP, the results of the two models were compared to see the effects of the feedback, outer and inner dependences of the elements. According to the result, ANP model give a relative importance for environmental and socio-economic benefits as a criteria of public transport development, however, the AHP model turned out to give importance for the capital cost and capacity. Providing Bus Rapid Transit and Light Rail are the chosen alternatives in the case of ANP, where as AHP model choose expanding the existing bus services. Key words: Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Public transportation projects 1. INTRODUCTION Decision-making in transportation projects considers interrelated criteria. Especially in Asian and African developing cities, decisions are made with capital cost constraints and some administrative influences. For example, the public transportation in Addis Ababa, the capital city of Ethiopia, has numerous problems related with socio-economic, political and financial issues. For several years the city has no well-integrated public transportation. There is only one Bus Company with limited fleet size, however, the population is growing every year and the socioeconomic settings are becoming complex. People s mobility pattern is changed with a change of land use and economic activities. Parallel to these changes, the public transport has not shown improvements. Buses and taxis are the only public transportation modes, which the residents are extensively using. Private car is not affordable for the majority of the residents. Therefore, urban transport improvement and development measures are important in providing an optimal transit in order to increase accessibility and coverage of public transportations. In the past, several researches have developed various public transportation improvement programs, and in this regard, providing a well-designed transport system with increased public transport mode choices

2 is indispensable. There have been some studies to propose public transport development programs in the city of Addis Ababa (e.g., ERA, 2005). However, implementation is not realized yet because of the multi-facet constraints. For that reason, multi-criteria decision making methods that can incorporate the conflicting considerations are essential. Therefore, this case study has an objective of applying the ANP model for prioritize public transport alternatives. Because the ANP process is based on deriving ratio scale measurements, it can be used to allocate resources according to their ratio-scale priorities. The Analytic Network Process (ANP), developed by Thomas L. Saaty, provides a way to input judgments and measurements to derive ratio scale priorities for the distribution of influence among the factors and groups of factors in the decision. ANP is the generalization of the Analytic Hierarchy Process (AHP). The basic structures are networks. Priorities are established in the same way they are in the AHP using pairwise comparisons and judgments (Jose Fugueira, 2005). The well-known decision theory, the Analytic Hierarchy Process (AHP) is a special case of the ANP. Both the AHP and the ANP derive ratio scale priorities for elements and clusters of elements by making paired comparisons of elements on a common property or criterion. The Analytic Network Process (ANP) is the most comprehensive framework for the analysis of societal, governmental and corporate decisions that is available to the decision-maker. It is a process that allows one to include all the factors and criteria, tangible and intangible that has bearing on making a best decision. The Analytic Network Process allows both interaction and feedback within clusters of elements (inner dependence) and between clusters (outer dependence) with respect to the control criteria. Through its supermatrix, whose elements are themselves matrices or column priorities, the ANP captures the outcome of dependence and feedback within and between clusters and elements (Thomas L. Saaty, 1999). Such feedback best captures the complex effects of interplay in human society, especially when risk and uncertainty are involved. ANP is a relatively simple, intuitive approach that can be accepted by managers and other decision-makers (Meade and Presley, 2002). ANP has been applied for various fields including transportation planning and management. Piantanakulchai (2005) applied ANP model for highway corridor planning. The research demonstrates how to empirically prioritize a set of alternatives by using ANP model. The paper first reviews the planning issues related to the highway corridor planning. Then related characteristics were used to structure the ANP model and scores were computed for prioritizing the potential highway alignments. Shang et.al (2004) explored the potential of applying the analytic network process (ANP) to evaluate transportation projects in Ningbo, China. In this current study, the ANP is implemented as a decision making tool for public transportation development programs. The following sections will explain the theoretical background of AHP and ANP, the problem description, the decision model, the pairwise comparison and the synthesis with benefit-cost analysis. 2. METHODOLOGY 2.1. Analytic Hierarchy Process (AHP) The Analytic Hierarchy Process (AHP) for decision structuring and decision analysis was first introduced by Saaty (Thomas L. Saaty, 1994; 1996 (1)). AHP allows a set of complex issues that

3 have an impact on an overall objective to be compared with the importance of each issue relative to its impact on the solution of the problem. AHP models a decision-making framework that assumes a unidirectional hierarchical relationship among decision levels. The top element of the hierarchy is the overall goal for the decision model. The hierarchy decomposes to a more specific attribute until a level of manageable decision criteria is met. The hierarchy is a type of system where one group of entities influences another set of entities (Meade and Presley, 2002). AHP was developed due to the need to include criteria that are not measurable in an absolute sense. The fact that AHP allows subjective judgments as well as quantitative information to enter into the evaluation process simultaneously and provides decision- makers with better communication make it an appealing decision-making aid (Shang et. al., 2004). The shortcoming of AHP is that many decision problems can not be structure hierarchically because they involve the interaction and dependence of higher level elements on lower level elements. Not only does the importance of the criteria determines the importance of the alternatives as in a hierarchy, but also the importance of the alternatives themselves determines the importance of the criteria (Thomas L. Saaty, 1996 (2)) 2.2. Analytic Network Process (ANP) It is a suitable multi-criteria decision analysis (MCDA) to evaluate alternatives. It is a generalization of the Analytic Hierarchy Process (AHP). The basic structures are networks. The feedback structure does not have the top-to-bottom form of hierarchy but looks more like a network, with cycles connecting its components of elements, which we can no longer call levels. A network has cluster or elements, with the elements being connected to elements in another cluster (outer dependence) or the same cluster (inner dependence). The priorities derived from the pairwise comparison matrices are entered as parts of columns of a supermatrix. The supermatrix represents the influence priority of an element on the left of the matrix on an element at the top of the matrix (Thomas L. Saaty, 1996, Jose Figueira et.al, 2005).Whereas AHP models a decision making framework that assumes a unidirectional hierarchical relationship among decision levels, ANP allows for more complex interrelationships among the decision levels and attributes (Thomas L. Saaty, 1999). Typically in AHP, the hierarchy decomposes from the general to a more specific attribute until a level of manageable decision criteria is met. ANP does not require this strictly hierarchical structure. Two-way arrows (or arcs) represent interdependence among attributes and attribute levels, or if within the same level of analysis, a looped arc. The directions of the arcs signify dependence. Arcs emanate from an attribute to other attributes that may influence it. The relative importance or strength of the impacts on a given element is measured on a ratio scale similar to AHP. A priority vector may be determined by asking the decision maker for a numerical weight directly, but there may be less consistency, since part of the process of decomposing the hierarchy is to provide better definitions of higher level attributes. The ANP approach is capable of handling interdependence among elements by obtaining the composite weights through the development of a supermatrix. (Meade and Presley, 2002, Thomas L. Saaty, 1999) 3. PROCEDURES OF ANP 1. Develop the decision model: it can be represented as a directed hierarchy (like AHP) or the hierarchy of networks with feedbacks. The relevant goal, criteria, alternatives, cost and benefits, considerations etc. form a cluster and each cluster may have its elements in it.

4 Elements are the entities in the system that interacts with each other. They could be a unit of decision makers, stakeholders, criteria or sub criteria (if exists), possible outcomes, and alternatives etc. In complex system which contains a great number of elements it would be very time consuming to measure relative importance of each element with every single element in the system. Instead, elements which share similar characteristics are usually grouped into cluster. The determination of relative weights mentioned above is based on pairwise comparison as in the standard AHP (Piantanakulchi, 2005) Cluster 1 GOAL Outer dependence Element 1 Element 2 Element 3 Feedback Criteria 1 Alternative 1 Criteria 2 Criteria 3 Alternative 2 Alternative 3 Cluster 2 Element 1 Element 2 Element 3 Cluster 4 Cluster 3 Element 1 Element 2 Element 3 AHP STRUCTURE Inner dependence Element 1 Element 2 Element 3 ANP STRUCTURE Figure 1 Framework of the AHP and ANP model 2. Perform a pairwise comparison among the clusters and elements interacting in the decision system using a scale of preference as given in table 1. Table 1 Scale of preference between two elements Level of importance Definition Explanation 1 Equally preferred Two activities contribute equally to the objective 3 Moderately Experience and judgment slightly favor one activity over preferred the other 5 Strongly preferred Experience and judgment strongly or essentially favor one activity over the other 7 Very strongly An activity is strongly favored over another and its preferred dominance demonstrated in practice 9 Extremely The evidence favoring one activity over another is of the preferred highest degree possible of affirmation 2, 4, 6, 8 Intermediate values Used to represent compromise between the preferences listed above Reciprocals Reciprocals for inverse comparison

5 When n elements in the cluster (e.g., criteria) are compared in a pairwise mode with respect to a common property or controlling element (e.g., goal element), n(n-1)/2 questions are needed to elicit value judgments from the decision maker and fill up the pairwise comparison matrix. 1 a 1n A=.... Where a ij = 1/a ji (1) a n Calculate the local priority weight (eigenvector) of the matrix obtained from step 2. Measure the consistency using the following formula: λ max n CR= ( n 1) RI (2) Where λmax is the principal eigenvalue, n is number of elements to be compared and RI is the random consistency index given in the following table that depends on n. Saaty recommends that the CR (consistency ratio) must be less than 0.1 to 0.2 for the judgment to be considered acceptable (Satty, 1980). n RI Form the initial supermatrix from the priority indices (eigenvectors) obtained in step 3. C1 C2 CN W= C1 C2 CN e 11 e 12. e 1n1 e 21 e 22. e 2n2. e N1 e N2..eNnN e 11 e 12 e 1n1 e 21 e 22 e 2n2 e N1 e N2 e NnN W 11 W 12 W 1N W 21 W 22 W 2N.... W N1 W N2 W NN W ij = w i1j1 w i1j2 w i1jnj w i2j1 w i2j2 w i2jnj w inj1 w inj2 w injnj W ij is the eigenvector or priority weight (3) 5. Transform the initial (unweighted) supermatrix to the weighted or stochastic supermatrix by cluster weighting and normalization so that the column sum equal to Finally compute the limiting priorities of the weighted supermatrix. This can be done by raising the weighted matrix to the large power until it converges to the limit. limw k (4) k Then, these priorities are normalized according to the clusters to provide the overall relative priorities.

6 4. PROBLEM DESCRIPTION AND BACKGROUND OF THE CASE STUDY AREA Addis Ababa is the capital city of the Federal Democratic Republic of Ethiopia, located in the centre of the country. Established in 1886, the city has experienced several planning changes that have influenced its physical and social growth. The area of Addis Ababa is square kilometers. The current population of the city is 2.57 million (2005 estimate), which is about 3.9 percent of the population of Ethiopia. It also represents about 26 percent of the urban population of Ethiopia. Addis Ababa has an aggregate population density of persons per square kilometer. Public transport in the city consists of conventional bus services provided by the publicly owned Anbessa City Bus Enterprise, taxis operated by the private sector, and buses used exclusively for the employees of large government and private companies. The role of bicycles in urban transport is insignificant because of topographic inconvenience (The World Bank African Region Scoping Study, 2002). Buses provide 40% of the public transport in the city where as taxis account for 60% (ERA, 2005). There is no rail transit within the city. Inner Zone Intermediate Zone Expansion Zone Addis Ababa Figure 2 The city of Addis Ababa The city is currently experiencing a horizontal growth, but the bus service has not exhibited growth proportionate enough to accommodate this. The analysis results of the transit availability indices show that only the center of the city is being served by the existing bus networks while urban expansion areas have low transit availability (Mintesnot & Takano, 2006). Taxis have many constraints in their operation including bad behavior of divers, excessive fare, and high accident rate. The road network of Addis Ababa is limited in extent and right of way. Its capacity is low, on-street parking is prevalent and the pavement condition is deteriorating. Despite a large volume of pedestrians, there are no walkways over a large length (63%) of the roadway network. This is a major concern, especially as it contributes to the increased pedestrian involvement in traffic accidents (10,189 accidents occurred in 2004) (ERA, 2005). The city s traffic and transportation problems are numerous and highly linked with the socio-economic condition of citizens, the financial and institutional matters, management and politics. The major problem is luck of well-integrated public transportation modes such as bus rapid transit and light rail transit. For the 3 million population city, providing a regular bus service with only one Bus Company of limited fleet size can not cater the demand.

7 Public transportation problems Buses are the only public transit mode in the city. There is no rail transit or bus priority lane. Only one Bus Company with limited fleet size is providing a service for 3 million Pop. Developing well-integrated public transportation in the city BRT LRT Conflicting considerations for public transport development Big gap b/n demand and supply Poor Accessibility Low bus frequency Poor bus information system Capital cost AHP?? ANP?? Figure 3 Problem description 5. FORMING THE HIERARCHICAL NETWORKS Based on the problem descriptions, the hierarchical networks are generated. Five links are created with nodes to be the goal, the criteria and the alternatives. The dominance between the three clusters (goal, criteria and alternatives) and among the elements is formulated. The goal, which is developing well-integrated public transportation in the city, is formed to have dominance on both the criteria and the alternatives. The feedback link is also created to weigh the criteria in terms of the alternatives. An inner dependence is formulated that the criteria capital cost would have relative dominance on the other criteria, as for a project under financial constraints, other criteria are also influenced by the capital cost. CC 4 Benefits 1 Cluster 2 CRITERIA EB CA Control hierarchy SEB Cluster 1 GOAL Cost Cluster 2 ALTERNATIVES EXB BRT LRT BRT-LRT LINK 1- Goal to criteria- outer dependence LINK 2- Criteria to alternatives- outer dependence LINK 3- Alternatives to criteria- feedback LINK 4- Among criteria- inner dependence LINK 5- Goal to alternatives- outer dependence Figure 4 Decision Model

8 Table 2 Clusters and elements in the decision model CLUSTERS ELEMENTS EXPLANATIONS Goal To develop an integrated public transportation in the city of Addis Ababa Criteria Capital cost (CC) Investment for construction, equipment, facilities etc. Capacity (CA) Carrying capacity to tackle the travel demand Environmental benefit (EB) Reduction of CO 2, noise, etc. Socio-economic benefit (SEB) Creating employment and other economic activities, social interactions, benefits in reducing accidents, etc Alternatives Expand existing bus Adding the number of buses and extending bus route networks service (EXB) to the urban expansion areas Introduce Bus Rapid Transit (BRT) Bus Rapid Transit development with bus priority lanes, having the existing bus networks as a feeder routes Introduce Light Rail Transit (LRT) Light Rail Transit development at high travel demand areas, having the existing bus networks as feeder routes The combination of BRT and LRT Implementing both the Bus Rapid Transit and Light Rail Transit, having the existing bus networks as feeder routes 6. PAIRWISE COMPARISON MATRIX Pairwise comparison is a method implemented to decision-making using AHP. To make a pairwise comparison, one needs a hierarchic or network structure to represent the problem, as well as pairwise comparisons to establish relations within the structure. The pairwise comparisons are the steps in AHP and ANP where the decision maker will compare two components at a time with respect to the upper level cluster or element. In the discrete case these comparisons lead to dominance matrices, from which ratio scales are derived in the form of principal eigenvectors. These matrices are positive and reciprocal, e.g., a ij =1/a ij. In this study 11 sets of pairwise comparisons are formulated for 5 identified links in which the relationship is created. Link I is the pairwise comparison between the goal and the criteria. It is an outer dependence, assuming the goal has dominance over the criteria. In Link II the pairwise comparison between the criteria and the alternatives is created. It is obvious that in any decision making process the criteria affect the choices. Link III is created to make a feedback influence from the alternatives towards the criteria. In real situations, unlike hierarchical considerations in AHP, the choices can have dominance on the criteria. Link IV is the inner dependence among the criteria. In this case only one criteria (capital cost) is chosen to have a dominance on the rest of the criteria. The last link, link V is the outer dependence between the goal and the alternatives. The detailed characteristics of the pairwise comparison are discussed in the next sections based on the aggregated (through consensus) responses of certain respondents Outer dependence between the goal and criteria In this case, the relative importance of the criteria with respect to the goal is formulated. Among the four chosen criteria, capital cost is found out to be relatively the most important consideration for developing integrated public transportation in the city. Knowing the financial situation of the national as well as the municipal governments, one may not be surprised that the capital cost is an important consideration for huge projects like public transport development in the city. The second important criterion is the capacity in which the proposed public transport would cater the existing high demand. The environmental benefit and socio-economic benefits got small value in terms of the goal.

9 Table 3 Pairwise comparison matrix of criteria in terms of goal Goal CC CA EB SEB e-vector CC CA 1/ EB 1/7 1/5 1 1/ SEB 1/7 1/ λmax= CI= CR= Outer dependence between the criteria and alternatives This step measures the relative preference of alternatives in terms of the criteria. This can answer which alternative is preferable in terms of each criteria and it directly leads to the synthesis, if AHP model is considered. In the case of ANP, further networks should be analyzed in order to get a synthesis result. According to the result of this pairwise comparison, expanding existing bus service is favored in terms of capital cost, as it is fairly cheaper than developing BRT or LRT. With respect to capacity, the combination of BRT and LRT is preferred as it can accommodate high number of users at a time. Environmental benefit favors LRT; coinciding with the real situation in which LRT uses electric power, so that it reduces gas emission. Socio-economic benefit favors the combination of BRT and LRT. Table 4 Pairwise comparison matrix of alternatives in terms of capital cost CC EXB BRT LRT BRT-LRT e-vector EXB BRT 1/ LRT 1/5 1/ BRT-LRT 1/7 1/5 1/ λmax= CI= CR= Table 5 Pairwise comparison matrix of alternatives in terms of capacity CA EXB BRT LRT BRT-LRT e-vector EXB 1 1/3 1/5 1/ BRT 3 1 1/3 1/ LRT / BRT-LRT λmax= CI= CR= Table 6 Pairwise comparison matrix of alternatives in terms of EB EB EXB BRT LRT BRT-LRT e-vector EXB 1 1 1/9 1/ BRT 1 1 1/5 1/ LRT BRT-LRT 5 3 1/ λmax= CI= CR= Table 7 Pairwise comparison matrix of alternatives in terms of SEB SEB EXB BRT LRT BRT-LRT e-vector EXB 1 1/3 1/3 1/ BRT 3 1 1/3 1/ LRT / BRT-LRT λmax= CI= CR=0.0997

10 6.3. Feedback between the criteria and alternatives Up to this step, the hierarchy of levels was discussed without considering the feedback and the inner dependence. However, in reality, there is the two way influence between the criteria and the alternatives. i.e. the alternatives affect the criteria too. That is what AHP can t do because of the inflexible nature of hierarchies, but possible in ANP. In this case study, the pairwise matrix to measure relative dominance of the criteria in terms of the alternatives is formulated. The criteria capacity is relatively important in terms of an alternative expanding the existing bus service. According to the previous AHP output, expanding the existing bus service is relatively cheap; therefore, the probable consideration would not be capital cost but tackling the existing demand. Capital cost is the first consideration with respect to alternative introducing BRT with the e- vector of The same dominance is observed in the case of alternative introducing LRT with the e-vector of Both alternatives require a huge investment for implementation, thus it is not surprising that the important factor for alternatives the combination of BRT and LRT is the capital cost. Table 8 Pairwise comparison matrix of criteria in terms of EXB EXB CC CA EB SEB e-vector CC 1 1/5 3 1/ CA EB 1/3 1/5 1 1/ SEB 3 1/ λmax= CI= CR= Table 9 Pairwise comparison matrix of criteria in terms of BRT BRT CC CA EB SEB e-vector CC CA 1/ EB 1/7 1/5 1 1/ SEB 1/5 1/ λmax= CI= CR= Table 10 Pairwise comparison matrix of criteria in terms of LRT LRT CC CA EB SEB e-vector CC CA 1/ EB 1/9 1/ SEB 1/7 1/ λmax= CI= CR= Table 11 Pairwise comparison matrix of criteria in terms of BRT-LRT BRT-LRT CC CA EB SEB e-vector CC CA EB SEB λmax= CI= CR=0.1190

11 6.4. Inner dependence among the criteria Among the criteria, it is understood that only capital cost could have dominance over the remaining criteria. For example, high capacity public transportation requires high investment and environmental friendly public transport also has to do with cost. Therefore, the inner dependence between the capital cost and other criteria is formulated. Leaving the dominance of capital cost over itself, capacity is found out to be important criteria that affect the criteria capital cost Table 12 Pairwise comparison matrix of CC in terms of other criteria CC CC CA EB SEB e-vector CC CA 1/ EB 1/5 1/ SEB 1/5 1/ λmax= CI= CR= Outer dependence between the goal and the alternatives Unlike hierarchical consideration of AHP, ANP allows the bottom-up relationship of cluster and elements. Not only the relative importance of the criteria in terms of goal is derived, but in real situation, the goal has a direct effect on the alternatives. The general vision of the project can be derived from this step. In this case study, Introducing the combination of BRT and LRT is favored with respect to the goal of developing an integrated public transportation in the city (evector = ). LRT got the second biggest e-vector (0.2633). Expanding the existing bus service is the least preferable with respect to the goal. Table 13 Pairwise comparison matrix of alternatives in terms of the goal BRT- e-vector Goal EXB BRT LRT LRT EXB 1 1/3 1/5 1/ BRT 3 1 1/3 1/ LRT / BRT-LRT λmax= CI= CR= INITIAL, WEIGHTED AND LIMITED SUPERMATRICES Recalling the theoretical explanation in step 4 of section 3, C N denotes the N th cluster, e Nn denotes the n th element in the N th cluster, and W ij block matrix consists of the collection of the priority weight vectors (w) of the influence of the elements in the i th cluster with respect to the j th cluster. If the i th cluster has no influence to the j th cluster then W ij = 0. The matrix obtained in this step is called the initial supermatrix. As stated earlier, the pairwise comparison is performed and the eigenvector obtained from cluster level comparison as well as the element level comparison (e.g., the criterion capital cost and other criteria) are used to form the initial supermatrix. The initial (unweighted) supermatrix can be transformed to the stochastic (weighted) supermatrix by cluster weighting and normalization so that the column sum equal to one (see table 15). The stable limiting priorities of the weighted supermatrix can be calculated by raising the stochastic supermatrix to a large power until it converges to the limit as indicated in equation 4.

12 Table 14 Initial supermatrix GOAL EXB BRT LRT BRT/LRT CC CA EB SEB GOAL EXB BRT LRT BRT/LRT CC CA EB SEB sum Table 15 Weighted supermatrix GOAL EXB BRT LRT BRT/LRT CC CA EB SEB GOAL EXB BRT LRT BRT/LRT CC CA EB SEB sum Table 16 Limited supermatrix GOAL EXB BRT LRT BRT/LRT CC CA EB SEB GOAL EXB BRT LRT BRT/LRT CC CA EB SEB SYNTHESIS AND DISCUSSION The target of the analysis is to synthesize the priorities of alternatives. The AHP model can be synthesized by considering only the three links (goal criteria alternatives) whereas in ANP the limiting priorities gives the result after normalizing the result according to clusters to provide the overall relative priorities. In the distributive mode, the weight of the alternatives or the criteria can be obtained from the limit supermatrix, which is normalized to yield a unique estimate of a ratio scale underlying the judgments. In ideal mode, the weights of the alternatives or the criteria

13 obtained from the limit supermatrix are divided by the value of the highest rated alternative. In this manner the newly added alternative that is dominated everywhere can not cause reversal in the ranks of the existing alternatives (Thomas L. Saaty, 1994) According to the AHP model, the importance of the capital cost exhibited with higher eigenvector (0.5655) followed by the capital cost (0.2802). This relative importance of the criteria in terms of the goal brought a synthesis result of choosing alternative 1 which is expanding the existing bus service in the city followed by the combination of BRT and LRT. Without considering the feedback (outer dependence) and the inner dependence, environmental and socio-economical benefits got a little attention with eigenvector of and respectively. It is obvious that, one who considers the capital cost would definitely go for the cheapest alternative instead of investing on BRT and LRT. However, with the consideration of inner dependence, feedback and outer dependence (ANP model), the relative importance of the criteria is changed (not in sequence but in value). The environmental and socio-economical benefits got a higher value which indicates that a strong relation between the cluster and elements give a more clear result. According to the ANP model, the combination of BRT and LRT got a first priority followed by introducing LRT. Expanding the existing bus service got the third ranking. Therefore, environmental and socio-economical consideration contributed for the change of the result. Table 17 Synthesized result ANP AHP Clusters and elements Raw Distributive Ideal Distributive EXB BRT LRT BRT & LRT Cluster sum CC CA EB SEB Cluster sum EXB BRT & LRT BRT Weight LRT ANP Figure 5 Synthesis results of alternatives AHP 0 CC CA EB SEB Criteria ANP AHP Figure 6 Criteria weights in terms of the goal

14 The study is extended to the benefit-cost analysis based in the eigenvector weights of the criteria except the cost. The estimated capital costs are normalized to be considered as a cost in the analysis. The priority weights of other criteria are considered as the benefit. Environmental and socio-economic benefits are originally designed as benefit, and the providing high capacity is added as a benefit of providing an integrated public transportation in the city. According to the result, the combination of BRT and LRT got higher benefit over the cost followed by expanding the existing bus. Alternatives Table 18 Benefit-Cost analysis Cost Benefit Average Estimated Normalized CA EB SEB benefit Benefit/cost EXB 15METB/km* BRT 35 METB/km* LRT 100 METB/km* BRT-LRT 75 METB/km* Sum 220 METB/km *METB/km = Million Ethiopian Birr (1USD = 8.8 ETB) *The costs for BRT and LRT are estimated by the Addis Ababa Master Plan Revision Office Benefit/cost EXB BRT LRT BRT-LRT Benefit/cost Alternatives Figure 7 Benefit-cost results 9. CONCLUSIONS In this paper, the multi-criteria decision making model is explored using supermatrix approach for public transport development programs. Analytic Network Process (ANP) is developed based on the hierarchical model (AHP) and the results are compared. The ANP signifies better the complex real-world problem as it allows for feedback and interdependency among various decision levels such as clusters and elements. The relative dominance of the criteria with respect to the goal can be shown clearly in ANP. The model can be developed further by performing a

15 multi-participant decision making process, by diversifying criteria, and the control hierarchy. Since the public transportation projects face diversified, conflicting and interrelated considerations, additional factors should be added to utilize the model fully. The decision maker should be carefully selected comprising the technical, political and community representatives. REFERENCE Ethiopian Roads Authority, ERA (2005) Urban Transport Study and Preparation of Pilot Project for Addis Ababa, Consulting Engineering Services (India) Private Limited and SABA Engineering Private Limited Company, Addis Ababa, Ethiopia, Jennifer S. Shang, Youxu Tjader, and Yizhong Ding (2004) A Unified Framework for Multi- Criteria Evaluation of Transportation Projects, IEEE Transactions on Engineering Management, Vol. 51, No. 3 Jose Figueira, Salvatore Greco and Mattias Ehrgott (2005) Multiple Criteria Decision Analysis, State of the Art, Springer Science + Management, Inc. Laura M. Meade and Adrien Presley (2002) R & D Project Selection using the Analytic Network Process, IEEE Transactions on Engineering Management, Vol. 49, No. 1 Mintesnot G. and S. Takano (2006) Application of Logical Planning Model for Public Transportation Improvement Programs in the City of Addis Ababa. Studies in Regional Science, Journal of JSRSAI, Vol. 36, No.3, pp Mongkut Piantanakulchai (2005) Analytic Network Process Model for Highway Corridor Planning, ISAHP-2005, Honolulu, Hawaii Thomas L. Saaty (1980) The Analytic Hierarchy Process. McGraw-Hill, Inc., New York, USA. Thomas L. Saaty and Luis G. Vargas (1994) Decision Making in Economic, Political, Social and Technological Environments, The AHP Series Vol. VII, RWS publications, Pittsburg, USA Thomas L. Saaty (1996) Multi-Criteria Decision Making, the Analytic Hierarchy Process, RWS publications, Pittsburg, USA Thomas L. Saaty (1996) Decision Making with Dependence and Feedback, the Analytic Network Process, 1 st ed. RWS Publications, Pittsburg, USA Thomas L. Saaty (1999) Fundamentals of the Analytic Network Process, ISAHP-1999, Kobe, Japan World Bank African region scoping study (2002) Urban Mobility in Three Cities, Addis Ababa, Dar Es Salam, Nairobi, SSATP Working Paper No. 70