For public transportation problem there are some analytic hierarchical processes for decision support, however there only very few applications which consider the interrelations between the public transport supply quality factors. Because representing the problem by the analytic network process is more similar to real situations where the factors act in a non hierarchical way. The paper aims to analyze the interrelation and the importance of relevant factors in public transportation systems by using the analytic network process, that support the decision makers to evaluate the impacts of different criteria in the final result.

1. 5th
International Conference
on Road and Rail Infrastructure
CETRA 2018
Sarbast Moslem, Szabolcs Duleba
Budapest University of Technology and Economics (BME)
Department of Transport Technology and Economics
An Application of Analytic Network Process (ANP)
for Evaluating Public Transport Supply Quality

2. Content
•Problem statement
•Methodology
•Results
•Suggestions
•Conclusion
•References

3. Problem Statement
•Public transportation development is decided by
decision makers (DM)s, to make decisions there are
some analytic hierarchical processes for that.
• However there only very few applications which
consider the interrelations between the public transport
supply quality factors.

4. •The paper aims to analyze the interrelation and the
importance of relevant factors in public transportation
systems by using ANP, that support the decision
makers to evaluate the impacts of different criteria in
the final result.

5. •The paper discuss the most important factors of supply
quality for public bus transportation system in
Budapest city (Hungary).

6. Methodology
MCDM applications growth is
getting bigger day by day over
the last 20 years. The following
MCDA or MCDM methods are
available, many of which are
implemented by specialized
decision-making software.

7. ANP
Most Applied MCDM Methods in Transportation
Projects
Fuzzy ANP
Fig. 1. Most Applied MCDM Methods in Transportation Projects

8. MCDM
Method Advantages Disadvantages
AHP
-Mathematically proven, eigenvector
method, methodology correct.
-Consistency in evaluation.
-Hierarchy is not always strict as
should to be.
-Interrelations between factors not
flexible.
ANP
-Mathematically proven, eigenvector
method, methodology correct.
-Interrelations between factors flexible.
-Enables the existence of
interdependences among criteria.
-interdependency and feedbacks of
different levels of the network.
- It is hard to fill up the supermatrix
by the public.
- When the decision structure is
basically hierarchal, then AHP from
mathematical point of view is more
effective.
TOPSIS
-Mathematically proven,
-Full use of attribute information
provides a cardinal ranking of options.
-ranking reversal
-correlations between criteria,
-uncertainty in obtaining the
weights only by objective methods
or subjective methods
PROMETHEE
-Mathematically proven, -Non flexibility of the software
package
ELECTRE
-Has a clearer view of alternatives by
eliminating less favorable ones.
-It only produces a core of leading
alternatives.

9. •There are many approaches of MCDM and each one
has advantages and disadvantages as we mentioned.
•In presented study ANP has been used, Because:
• Our problem is more non-hierarchal (network) structural,
• Consistency check is required
• We do not only have ranking one factor related to other, but also ranking
of scours by using the weights
• Pairwise comparisons (PC)s had to be made by the evaluators
for all the elements of the model.

10. Criteria Explanation
C1 “Service Quality”, everything excluding transport it self
C2 “Transport Quality”, for real time on vehicle
C3 “Tractability”, getting information from every aspect
C4 “Physical comfort”, comfort of seat, crowdedness, condition air
C5 ”Mental comfort”, contains environmental aspects and behavior of driver
C6 “Safety of travel”, feeling in safe, accidents in the bus, security
C7 “Perspicuity”, clear understanding for schedule and information
C8 “Information before travel”, amount and quality of information
C9 “Information during travel”, availability, quantity and quality of information
C10 “Approachability”, of the service before beginning of travel, ticketing services
C11 “Directness” reaching the destination without shifting vehicles
C12 “Time availability” the time frame when using certain vehicle
C13 “Speed”, speed for the time of whole travel process
C14 “Reliability”, the quality of being trustworthy
C15 “Directness to stops”, reaching the stops for travel
C16 “Safety of stops”, subjective feeling
C17 “Comfort in stops”, heating and cooling systems, seats
C18 “Need of transfer”, do passenger has to change or not
C19
“Fit connection”, between bus lines or between other type of public transportation and bus
lines, guarantee of transfer
C20 “Frequency of lines”, working hours based on schedule
C21 “Limited time of use”, a part of the whole travel process
C22 “Journey time”, related to speed of the vehicle, (get on_ get off)
C23 “Awaiting time”, waiting for public transport
C24 “Time to reach stops” a part of the whole travel process

11.  276 PCs have been evaluated by 10 transport experts.
 (2760 PCs have been analyzed).

12.  For determining the
eigenvectors of the
aggregate matrices
the following method
was applied:

13. Fig 2. The interdependent relationship among the public transport supply quality criteria (source: own)

14. Fig 3. Supermatrix in ANP (Source: Saaty, 1996)
The geometric mean has been
applied to aggregate
evaluators answers

15. Results
•The study was made to evaluate the situation of
Budapest’s public bus transport system.
•The characteristics of the conducted survey based on the
network model.

16.  The evaluation has been done by Judgment scale of
relative importance for PC (Saaty’s 1-9 scale).
 The analytic network analysis has been designed in
SuperDecisions software to get the final scores.

17. Numerical
values
Verbal scale Explanation
1 Equal importance of both factors Two elements contribute equally
3
Moderate importance of one factor
over another
Experience and judgment favour one
factor over another
5
Strong importance of one factor over
another
An factor is strongly favoured
7
Very strong importance of one factor
over another
An factor is very strongly dominant
9
Extreme importance of one factor over
another
An factor is favoured by at least an order
of magnitude
2,4,6,8 Intermediate values
Used to compromise between two
judgments
Table 1. Judgment scale of relative importance for PC (Saaty’s 1-9 scale)

18. Rank Criteria Normalized Scores Idealized Scores
1 C3 0,099318574 1
2 C1 0,075111638 0,756269801
3 C2 0,0736722 0,741776659
4 C8 0,060192025 0,606050027
5 C7 0,054193171 0,545649907
6 C23 0,053153161 0,535178453
7 C12 0,050342843 0,506882454
8 C11 0,045123964 0,454335598
9 C6 0,04483899 0,451466309
10 C16 0,041168056 0,414505103
11 C4 0,038746472 0,390123121
12 C20 0,038211198 0,38473366
13 C22 0,035720337 0,359654144
14 C19 0,034160844 0,34395222
15 C10 0,033079581 0,333065402
16 C9 0,03276281 0,329875956
17 C5 0,03117803 0,313919431
18 C13 0,030828966 0,310404836
19 C21 0,026397867 0,265789832
20 C24 0,024273387 0,244399275
21 C14 0,023484878 0,236460083
22 C18 0,019125931 0,192571542
23 C15 0,017946619 0,180697505
24 C17 0,016968459 0,170848795

19.  151 Interrelations between different criteria have
been detected regarding to aggregating the answers.
(The network model contains (276-151= 125 non interrelations))
 Tractability was the most important criteria
depending on the applied analysis.
 The second important criteria was Service quality
followed by Transport quality.

20.  The preferences make decisions more flexible and
support DMs to solve the variety of transportation
problem.
 During ANP approach, the consistency of answers has
been examined by Saaty’s Consistency Index (CI),
and Consistency Ratio was (CR) < 0.1.

21. Suggestions and Conclusion
 The application enables DMs to better understand
for the complex relationships of the relevant
criteria in the decision-making, that subsequently
improve the reliability of the decision.

22.  Applying ANP approach is quite complicated than
other approaches, because of its large number of
pairwise comparisons (276 PCs), and the
inconsistency check also difficult due to the
Supermatrix.

23.  Participated experts stated that ANP survey is
quite complicated and require long time to evaluate
the criteria, due to the large number of PC.
 For further researches, another approach is
recommended to be applied regarding the detected
interrelations between the criteria.

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25. moslem.sarbast@mail.bme.hu