Tehran, capital city of Iran, is one of the larger cities of the world
with a population of around 7 million (Fanni, 2006). During the first
40 years its population increased by only 50,000 people, but from 1956
to 2006 its population has increased by more than seven million people
and it has transformed itself to a large metropolis.
Tehran=s planning history shows early stages in which new infrastructure
was designed and developed by the government as part of its strategy for
modernization and growth management. It results producing Tehran=s comprehensive
plan in 1968. The period of reconstruction in the 1990s relaxed some of
the limits of the 1968 plan, which showed the urgent need for an updated
planning framework. Several planning documents which were launched during
this period have emphasize on considering stronger role for the municipality
and more attention to the policy developments. A firm of Iranian consultants
was commissioned in 1985 to prepare a plan for the period of 1986-1996.
After much delay, it was only in 1993 that the plan was finally approved
by the Urban Planning High Council. This plan also focused on growth management
and a linear spatial strategy, using the scales of urban region, sub-region,
district, area and neighborhood. It proposed that the city be divided
into 22 districts within five sub-regions, each with its own service centre.
Based on Zebardast (2006), the 1993 plan was not welcomed by the municipality,
which disagreed with its assessments and priorities, finding it unrealistic,
expensive and impossible to implement. The municipality produced its own
strategic plan for the period 1996-2001, known as Tehran Municipality=s
First Plan, or Tehran 80. Rather than introducing a land-use plan as its
goal, this was the first plan for the city that emphasized a set of strategies
and proposed policies to achieve them. It identified the city=s main problems
as shortage of resources to deliver its services, the pace and pattern
of urban growth, environmental pollution, the absence of effective public
transport and inefficient bureaucracy. The municipality=s vision for the
future of the city was then outlined to have 6 major characteristics:
a clean city, ease of movement in the city, the creation of parks and
green spaces, the development of new cultural and sports facilities, reform
of the municipal organization and planning for the improvement of urban
space, including preparation of comprehensive and detailed plans for land
use and conservation (Zebardast, 2006). These plans have remained unimplemented
and the city continues to suffer from a range of problems, including traffic
congestion, environmental pollution and unaffordable property prices (Madanipour,
2006). Since, 2004, Tehran Municipality has started to develop new detailed
urban land use plans and several consultant companies are working on a
strategic plan to link these detailed plans and to guide the future development
of the city as a whole.
This research intends to identify major factors resulting failure of
urban plans, using a Delphi method. It also utilizes AHP technique to
determine the significance of the factors in comparison with each other.
Following, the concepts of AHP and Delphi methods have been presented.
Then the results of implementing the Delphi-AHP method in a case study
for Tehran has been described and discussed.
MATERIALS AND METHODS
Case study: Tehran, the capital and largest city of Iran, has
been selected as the case study area. Tehran is a sprawling city at the
foot of the Alborz Mountain. In the 20th century, Tehran faced a large
migration of people from all around Iran. Some background related to Tehran
planning activities were mentioned at introduction part. This study was
conducted the situation of Tehran urban planning activities till 2007.
Delphi method: Delphi method is an iterative process designed
to achieve consensus among a group of experts on a particular topic. The
Delphi method is the most effective means of querying experts to identify
factors causing urban detailed plan implementation failure. This is especially
useful in situations where no standard criteria exist for evaluation.
The method is widely used in many studies and various explanations and
examples are available (Dijk, 1990; Hafeznia et al., 2008; Hosseinali
and Alesheikh, 2008; Linstone and Turoff, 2002; Okoli and Pawlowski, 2004;
Shiftan et al., 2003). In this research the Delphi method is used
as a framework to determine the major factors influencing urban plan implementation
failure as well as the preference of the factors.
According to Linstone and Turoff (2002), the Delphi process today exists
in two distinct forms: Conventional Delphi and Real-lucre Delphi (Delphi
Conference). Although the latter approach is often preferred, in this
research the Conventional Delphi approach was used because of limitations
for collecting all respondents spontaneously at a special place. Conventional
Delphi method comprises the following steps (Fowles, 1978; Fischer, 1978):
||Design the questionnaire and select the experts;
||Perform the first round survey of anonymous experts;
||During the first round survey, provide the experts with opinion
of the others;
||According to survey of the first round, request that each expert
answer again the first round problem while observing whether new solutions
are proposed or different perspectives are set forth;
||Synthesize expert opinions and reach a consensus.
||Repeat steps iii and iv until a uniform result is achieved for a
AHP method: AHP method, developed by Saaty (1980), has been studied
extensively and used in numerous applications in the last 20 years (Ho,
2008; Cheong et al., 2008). The wide AHP applicability is due to
its simplicity, ease of use and great flexibility.
As a decision method that decomposes a complex decision problem into
a hierarchy, AHP is also a measurement theory that prioritizes the hierarchy
and consistency of judgmental data provided by a group of decision makers
(Wu et al., 2007). AHP incorporates the evaluations of all decision
makers into a final decision by pair-wise comparisons of the alternatives
(Saaty, 1980). AHP has been successfully applied to a diverse array of
problems, with the calculation procedure as follows:
To establish the pair-wise comparison matrix A, Let C1, C2,Y,
Cn denote the set of elements, while aij represents
a quantified judgment on a pair of elements Ci and Cj.
The relative importance of two elements is rated using a scale with the
values are presented at Table 1.
This yields an nxn matrix A as follows:
where, aii = 1 and aij = 1/aji ; i,j
= 1,2, Y, n. In matrix A, the problem becomes assigning to the n elements
C1, C2, Y,Cn a set of numerical weights
W1,W2,Y,Wn that reflect the recorded
||Saaty=s 1-9 scale for AHP preference (Saaty, 2006)
If A is a consistency matrix, the relations between weights Wi
and judgments aij are simply given by Wi/Wj=
aij (for i,j =1, 2,Y, n) and assigned relative weight enters
into the matrix as an element aij and reciprocal of the entry
1/ aji goes to the opposite side of the main diagonal.
The preferences presented by each individual expert should be aggregated to
obtain a single weight for each factor. Geometric mean method (GMM) is a commonly
used method in AHP to aggregate judgments of individuals within a group (Aull-Hyde
et al., 2006). The geometric mean is consistent and satisfies the four
axioms underlying the AHP theory (Escobar et al., 2004). The geometric
mean is a summary statistic useful when the measurement scale is not linear.
Given values X1,X2,X3, Y,Xn, the
geometric mean of these n values is given by: [X1, X2,
X3, Y, Xn]1/n Seven experts as representative
of seven consultant companies supplied the survey asking for a group decision
making methodology. Therefore, methodology and concept developed by Escobar
et al. (2004) and Xu (2000) was used in this research to aggregate individual
preferences; generate the aggregated comparison matrix and compute group consistency
of the individual weights.
As regards group decision making, AHP considers two different approaches:
the aggregation of individual judgments (AIJ) and the aggregation of individual
priorities (AIP) (Rigopoulos et al., 2008). The AIP is used in
this research. It has been proved that using the eigenvector method (EM)
as the prioritization procedure and the Weighted Geometric Mean Method
(WGMM) as the aggregation procedure, if the individual decision makers
have an acceptable inconsistency when eliciting the judgments, results
in an acceptable group decision making (Escobar et al., 2004).
One important property of the geometric mean is its ability to dampen
the effect of very high or low values; whereas, such very high or very
low values might bias the arithmetic mean. In other words, the geometric
mean is less affected by extreme values than the arithmetic mean.
RESEARCH AND RESULTS
The research consisted of five main steps that each is discussed further:
||Defining major factors causing urban plans implementation
||Establishing the judgment matrix,
||Calculating the significance of the factors for each experts,
||Testing consistency of each expert judgments and
||Aggregating expert judgments.
Defining major factors causing urban plans implementation failure:
In this stage, in adoption of Taleai et al. (2007) and Hsu et
al. (2007) a modified Delphi technique is used. To elicit expert=s
opinions, an open questionnaire is used to identify each factor and additionally,
literature review and expert interviews integrate recurrent opinions expressed
in the Delphi survey.
An integrated questionnaire survey, inspection and interview approach
was adopted, in this stage. Although the questionnaire satisfied all information
required for the assessment, the inspection and interview could provide
the authors with better understanding of the current situation. To do
the assessment, seven experts as representative of 7 consultant companies
working with different regional municipalities of Tehran were contributed.
||The attendees were interviewed,
||A questionnaire was completed or attendees were requested to complete
and get it back later,
||Major factors causing urban plan implementation failure were defined,
The collected information was then integrated and analyzed. With such
a comprehensive study, different factors that affect implementation of
urban plans were determined as follows:
||Insufficient skilled personnel within municipalities
and private consultant companies,
||Inadequate attention to all factors existed in reality,
||Inadequate attention to Intelligence stage of planning process and
preparing the plans with insufficient insight and information,
||Ambiguity concerning the future trend of city development,
||Insufficient use of new technologies and tools such as GIS, SDSS/SPSS,
||Using traditional planning methods resulting preparing static plans
without any revision during next 5 to 10 years,
||Lack or unavailability of required data for decision making,
||Non-involvement of other urban management organizations (i.e. utilities
companies) for preparing the urban plans,
||Less attention to public participation planning approach in preparing
||Unsuitable organizational structure for implementing the urban plans
Establishing the judgment matrix: At the second stage, in the
context of another survey, different experts were individually requested
to rank and priorities the factors. Finally, the outcomes were analyzed.
AHP was used as the methodology for ranking and analysis.
Each expert makes a judgment matrix of the decision elements (major factors
have been determined at previous stage) and assigns them relative scores
based on AHP method. Table 2 shown the result, captured
from one of the experts.
Determining the individual priorities: EM as the prioritization
procedure and the GMM as the aggregation procedure were used in this stage
to determine the significance of the factors respected to each expert
Testing consistency of each expert judgments: The consistency
measures used for the EM in AHP is the consistent index (CI) proposed
by Saaty (1980). Let A=(aij) be an nHn judgement matrix and ω=(ω
1,ω 2,Y,ω n) be its priority
vector, where ωi>0, Σiωi=1. The
consistency in AHP is defined as the cardinal transitivity between judgments,
that is to say, aijajk=aik for all i,
j, k. The expressions for this measure are:
where, eij=aijωj/ω i,
i,j=1,Y,n. RI is Random Consistency Index that is shown in Table
If the value of Consistency Ratio is smaller or equal to 10% (CR<0.1),
the inconsistency is acceptable. If the Consistency Ratio is greater than
10% (CR>0.1), we need to revise the subjective judgment.
The result is shown that all individual judgment matrixes, except one,
have an acceptable CR. Therefore, the priority vectors of 6 experts have
been used at the next stage (Table 5).
Aggregating the expert judgments: As regards group decision making,
the aggregation of individual priorities (AIP) were used to aggregate
the opinion of all experts.
||Judgment matrix from one of the experts
||Priority vectors of all experts
||Random Consistency Index (RI)
||Consistency Ratio (CR) values for all experts
As it can be seen from Table 3, there is not complete
agreement within the experts on all factors. In order to aggregate all
opinions into one in such a way that the majority of opinions are considered,
the geometric mean method was applied. Last column of Table
3 presents the result.
Results: The results from the case study demonstrate that among
the determined factors, 4 are more important, respectively:
||Lack or unavailability of required data for decision
making especially at intelligence part of planning procedure;
||Unsuitable organizational structure for implementing the urban plans;
||Non-involvement of other urban management organizations (i.e. utilities
companies) for preparing the urban plans and
||Less attention to public participation planning approach in preparing
Several Tehrans comprehensive plans developed during last decades were
not welcomed by the municipality and failed in their implementation phase.
As a result, the city continues to suffer from a range of problems, including
traffic congestion, environmental pollution and unaffordable property
To assess influencing factors can result in failure of implementing urban
plans as well as their priorities, in the study a Delphi-AHP combined
method was developed to define and prioritize these factors. The results
show that the method is robust in clarifying different factors influencing
urban plan implementation activities. This study describes that the Delphi
method is a useful tools to querying and to achieve consensus among a
group of planners on the major factors causing urban detailed plan implementation
failure. AHP method was found useful, because the preference of all participants
regarding each determined factors, were transformed into a numerical scale
and were aggregated to produce a numeric indicator that can use to prioritize
factors causing urban detailed plan implementation failure. In addition,
the required pairwise comparisons forced the planners and decision makers
to think precisely over the various factors affecting urban plan implementation.
Finally, we conclude that the Delphi-AHP approach is beneficial to define
significant problems can cause failure of urban plans during their implementation.
It is instrumental in identifying specific factors in from different perspectives.
Authors would like to acknowledge the participation of the respondents
in this research.