# Selecting the appropriate public transportation system to access the Sari International Airport by fuzzy decision making

- GholamAli Shafabakhsh
^{1}, - Mansour Hadjihoseinlou
^{2}and - Seyed Ali Taghizadeh
^{1}Email author

**6**:128

https://doi.org/10.1007/s12544-013-0128-7

© The Author(s) 2013

**Received: **22 April 2013

**Accepted: **3 December 2013

**Published: **20 December 2013

## Abstract

### Introduction

Air travel is the fastest way for transporting goods and human; therefore, the airports are vital parts of transportation industry. Since airports are considered as tourism gates, they have a great influence on tourism sector and sub-domains economic. Sari is a city with many tourist attractions and has been known as the center of tourism in the north of Iran. Unfortunately because of the relatively long distance between Sari International Airport and the city and suburbs besides the lack of the proper access to these areas, tourists and businessmen didn't consider it as beginning or ending point of their travels, so Sari International Airport missed its deserving air traffic. The goal of this research is to find the appropriate public transportation system amongst options to access this airport. This is the first time that the presented method is being used for selecting one public transportation system to access airport.

### Method

This research has been done by using paired comparison for effective parameters of this selection by the decision makers, and determining the weight of parameters by using the Mikhailov method in fuzzy analytical hierarchy process.

### Conclusion

Finally the train system was proposed as the most appropriate public transportation system by using TOPSIS technique.

## Keywords

## 1 Introduction

Airports are essential resources for contemporary living. They play a major role in transporting people and goods around the world. Furthermore, airports connect air travelers with other modes of transportation. Therefore, an airport can be seen as a node that is connected to ground travel. Because air and ground systems are interrelated, they affect each other [1]. The goal of this research is to take effective steps in improving the airport’s air traffic by considering the tourist attraction potential in Sari International Airport and the difficulties to access this airport for passengers, for either business and non-business travels and also airport’s employees and staffs. This made us to have a look at incorporated solutions in important airports around the world and the current scientific methodologies in this area to propose an appropriate public transportation system considering airport’s present conditions and take a glance at the future to access this airport. Since public transportation mode choice is a cumbersome and time-consuming process with multiple constraints. This paper presents a simplified methodology for selecting an appropriate public transportation system by using an integrated multiple criteria decision making^{1} process.

## 2 Literature review

Harvey (1986) investigated the factors that influencing the choice of airport access mode for travelers who living in the San Francisco Bay Area. He separated travelers into two groups of business and non-business travelers, and concluded that access time and access cost are the most important parameters influencing airport access mode choice [2]. Pels et al. (2001) investigated the relationship between non-business and business travelers on the choice of access mode. They found that importance of access time for a business trips is higher than for non-business trips [3]. Chebli et al. (2003) investigated the effects of comfort that supplied by different access modes on mode choice behavior [4]. Hess (2004) developed a model to analyze a passenger’s access mode choice to airport. He showed that ticket price was not an important parameter for business travelers [5]. Tam et al. (2006) investigated the choice of access mode to Hong Kong International Airport and found that service performance like comfort is one of the important factor on access mode choice to airport. They also showed the reliability of transportation mode is one of the most important factor on airport access mode choice. Reliability to transportation mode is the difference between the actual time and the expected time of a mode to get to the airport [6]. Jou et al. (2011) by investigating and evaluating present transportation systems in Taiwan International Airport and the influence of the construction of new transportation system (MRT) on the behavior of the passengers in choosing the type of the system, have demonstrated that the time spent inside the vehicle and outside the vehicle is one of the important parameters in selecting the mode of the transportation system for getting to the airport. The total saved time and a calm and friendly environment are another important parameters for choosing a system [7]. Alhussein (2011) interpreted the mode choice to access King Khaled Airport of Saudi Arabia and by studying different types of present transportation systems stated that parameters such as time, access cost, reliability, and comfort are the most important parameters in choosing the transportation modes by the passengers [1]. Tsamboulas et al. (2012) studied the Athens International Airport employees travels to and back this airport through stated and revealed performance data and by interpreting the present conditions, demonstrated that time and cost of the travel and the salary of the employees are the main parameters in choosing the travel mode, and also demonstrated that metro and train with reasonable ticket price and travel time can attract a big share of the employees travels [8]. Meyer et al. (1984) in addition to considering economical parameters such as construction costs, operation costs, maintenance costs and travel costs of different public transportation modes, has also investigated these systems from viewpoints of services, travel time, capacity, and performance characteristics [9]. Zhi-Ping Fan et al. (2001) have investigated the multi criteria decision by information and data, and prioritized the options. One new view for solving the MADM problem where the decision maker states his/her priorities fuzzily has been proposed. For reflecting the decision maker’s prioritizing data, one optimization model is being created to estimate the weight of criteria and then select the most appropriate option [10]. Gumus (2009) by considering this fact that hazardous waste may threaten the health of humans and the environment, and its safe transport is important, mentioned that choosing the right transportation company would be one of the important problems of hazardous waste generators. This researcher based on the two step method for evaluating and choosing the transportation companies of hazardous waste, did his research by using Fuzzy-AHP and TOPSIS methods [11]. Bashiri et al. (2011) based on the fact that managers in constructing parameterizes are facing numerous problems such as choosing the technology of the construction of the company, choosing the strategy of maintenance of the parameter, relocation of machines, investigation of the quality of performance, choosing the seller and etc., stated that the multi criteria decision making is one of the tools for decision making and can help the managers in making accurate decisions [12]. Shelton et al. (2009) showed Transportation projects prioritization is a cumbersome and time consuming process. They investigated a simplified methodology for ranking transportation projects by using an integrated multiple criteria decision making process for prioritizing transportation projects when there are groups of decision makers with multiple opinions and biases [13].

## 3 Research method

## 4 Model

Regarding the geographical location of Sari International Airport which connected to Sari-Neka highway on the south and Goharbaran tourist road and tourist areas of Dashtenaz on the north, and also located along the Tehran-Shomal railroad, can bring it as a strategic airport in Iran. Unfortunately Sari International Airport not only doesn’t handle adequate number of air traffic, also caused lots of problems for airport employees that have to travel to this airport daily, because of inappropriate accessibility. The only modes to access this airport are private cars and taxi.

By considering this airport conditions and present infrastructures, these modes have been proposed for Sari airport by this research: train, bus with 25 passenger capacity and van shuttle with 5 seats and a space for passengers’ luggage. Since the distance between Sari International Airport and main railroad is about 1.5 km, and with a look to todays and the future conditions of this airport, which is located in a strategic tourism, business, and military area, it is possible to consider the construction of a train station in order, accessibility to this airport and transportation of goods become easier and more probable.

### 4.1 Creating the hierarchy of criteria and alternatives

### 4.2 Preparing the questionnaires and performing paired comparison

Sample completed questionnaire

Number | Criteria | Criteria | Importance | ||
---|---|---|---|---|---|

1 | Easy access to system | □ | Reliability | ■ | B |

2 | Easy access to system | □ | Access cost | ■ | D |

3 | Easy access to system | □ | Access time | ■ | D |

4 | Easy access to system | □ | Comfort | ■ | C |

5 | Easy access to system | ■ | Construction and operation cost | □ | D |

6 | Easy access to system | □ | Safety | ■ | D |

7 | Easy access to system | ■ | Interest to system | □ | C |

8 | Easy access to system | □ | Time headway | □ | B |

9 | Reliability | ■ | Access cost | □ | B |

10 | Reliability | □ | Access time | ■ | B |

… | ….. | □ | ….. | □ | … |

36 | Interest to system | □ | Time headway | ■ | B |

Verbal scales for stating the degree of importance

Importance | Verbal scales for stating the degree of importance | Triangle fuzzy numbers | Reverse triangle fuzzy numbers |
---|---|---|---|

A | Equivalent important (EI) | (1/2, 1, 3/2) | (2/3, 1, 2) |

B | Weak more important (WMI) | (1, 3/2, 2) | (1/2, 2/3, 1) |

C | Strong more important (SMI) | (3/2, 2, 5/2) | (2/5, 1/2, 2/3) |

D | Very strong more important (VSMI) | (2, 5/2, 3) | (1/3, 2/5, 1/2) |

E | Absolute more important (AMI) | (5/2, 3, 7/2) | (2/7, 1/3, 2/5) |

A sample of formation of preference matrix

Easy access to system | Reliability | Access cost | Access time | Comfort | Construction cost | Safety | Interest to system | Time headway | |||||||||||||||||||

Easy access to system | 1 | 1 | 1 | 0.4 | 0.5 | 0.667 | 0.5 | 0.67 | 1 | 0.4 | 0.5 | 0.667 | 0.5 | 0.67 | 1 | 0.5 | 0.667 | 1 | 1.5 | 2 | 2.5 | 2 | 2.5 | 3 | 1.5 | 2 | 2.5 |

Reliability | 1.5 | 2 | 2.5 | 1 | 1 | 1 | 1 | 1.5 | 2 | 0.5 | 1 | 1.5 | 0.5 | 1 | 1.5 | 0.333 | 0.4 | 0.5 | 1.5 | 2 | 2.5 | 2 | 2.5 | 3 | 1.5 | 2 | 2.5 |

Access cost | 1 | 1.5 | 2 | 0.5 | 0.667 | 1 | 1 | 1 | 1 | 0.5 | 0.667 | 1 | 1 | 1.5 | 2 | 0.333 | 0.4 | 0.5 | 1 | 1.5 | 2 | 2 | 2.5 | 3 | 2.5 | 3 | 3.5 |

Access time | 1.5 | 2 | 2.5 | 0.5 | 1 | 1.5 | 1 | 1.5 | 2 | 1 | 1 | 1 | 0.5 | 1 | 1.5 | 1 | 1.5 | 2 | 1 | 1.5 | 2 | 1.5 | 2 | 2.5 | 1.5 | 2 | 2.5 |

Comfort | 1 | 1.5 | 2 | 0.5 | 1 | 1.5 | 0.5 | 0.67 | 1 | 0.5 | 1 | 1.5 | 1 | 1 | 1 | 0.5 | 0.667 | 1 | 0.5 | 1 | 1.5 | 1.5 | 2 | 2.5 | 2 | 2.5 | 3 |

Construction cost | 1 | 1.5 | 2 | 2 | 2.5 | 3 | 2 | 2.5 | 3 | 0.5 | 0.667 | 1 | 1 | 1.5 | 2 | 1 | 1 | 1 | 0.5 | 0.67 | 1 | 1.5 | 2 | 2.5 | 2.5 | 3 | 3.5 |

Safety | 0.4 | 0.5 | 0.667 | 0.4 | 0.5 | 0.667 | 0.5 | 0.67 | 1 | 0.5 | 0.667 | 1 | 0.5 | 1 | 1.5 | 1 | 1.5 | 2 | 1 | 1 | 1 | 1.5 | 2 | 2.5 | 1.5 | 2 | 2.5 |

Interest to system | 0.333 | 0.4 | 0.5 | 0.333 | 0.4 | 0.5 | 0.333 | 0.4 | 0.5 | 0.4 | 0.5 | 0.667 | 0.4 | 0.5 | 0.67 | 0.4 | 0.5 | 0.67 | 0.4 | 0.5 | 0.67 | 1 | 1 | 1 | 0.5 | 0.67 | 1 |

Profile information of participants in questionnaires

University professor at transportation engineering | 5 |

Ph.D. student at transportation engineering | 4 |

MSc on transportation engineering | 11 |

Airport specialists and authorities | 13 |

Employees and passengers inhabited in city of Sari that have used Sari International Airport as a point of their travel at least 5 times | 28 |

### 4.3 Calculation the weight of criteria

_{ T }was calculated. The comparison of the resulted weights is presented in Fig. 9.

It is noticeable that the safety is the most important parameter from the view of transportation experts, airport specialists and authorities, passengers and employees, and the least important parameter is the construction and operating costs.

### 4.4 Calculation of the compatibility of questionnaires

All the calculations related to hierarchical analysis process based on judgment of decision makers who was stated in a paired matrix template. Any type of error and incompatibility in comparisons and determining the importance of criteria effects the final results. The compatibility value, is a tool which determines the compatibility and shows how much the resulted weights are reliable. Mikhailov stated that it is possible to use **λ** as a proper criteria for measuring the compatibility of comparisons and a **λ** approximately more than 1, shows the compatibility value of comparisons [15].

The compatibility ratio in sample questionnaire

α | λ | α | λ |
---|---|---|---|

0 | 1.0118442 | 0.6 | 0.9955634 |

0.1 | 1.0090460 | 0.7 | 0.9929570 |

0.2 | 1.0062838 | 0.8 | 0.9903769 |

0.3 | 1.0035564 | 0.9 | 0.9978222 |

0.4 | 1.0008616 | 1 | 0.9949676 |

0.5 | 0.9981976 |

Since for values of α > 0.4, the compatibility ratio was more than 1 (λ > 1), then the average value of α = 2 for 0 < α < 0.4, is a suitable factor for calculating the upper and lower bounds in Mikhailov model for calculating weight of criteria. From the 61 questionnaires, 14 of them were considered incompatible, which means for all value of α, the value of λ was less than 1, which were removed from the prioritizing process.

### 4.5 Final prioritizing of the systems based on TOPSIS method

Filled sample of questionnaire II

System/Criteria | Train | Bus | Van Shuttle |
---|---|---|---|

Easy access to system | 50 | 60 | 70 |

Reliability | 90 | 60 | 70 |

Access cost | 20 | 40 | 65 |

Access time | 40 | 70 | 50 |

Comfort | 40 | 60 | 80 |

Construction and operating costs | 40 | 15 | 10 |

Safety | 90 | 50 | 30 |

Interest to system | 45 | 60 | 70 |

Time headway | 90 | 55 | 35 |

### 4.6 Performing TOPSIS method

- Step I:By using \( {n}_{ij}=\frac{r_{ij}}{\sqrt{{\displaystyle \sum_{i=1}^m{r}_{ij}^2}}} \) equation, normalized matrix of N
_{DM}is calculated.$$ {\mathrm{N}}_{\mathrm{DM}}=\left[\begin{array}{ccccccccc}\hfill 0.612\hfill & \hfill 0.623\hfill & \hfill 0.308\hfill & \hfill 0.465\hfill & \hfill 0.497\hfill & \hfill 0.809\hfill & \hfill 0.730\hfill & \hfill 0.565\hfill & \hfill 0.671\hfill \\ {}\hfill 0.478\hfill & \hfill 0.490\hfill & \hfill 0.522\hfill & \hfill 0.682\hfill & \hfill 0.481\hfill & \hfill 0.480\hfill & \hfill 0.543\hfill & \hfill 0.505\hfill & \hfill 0.460\hfill \\ {}\hfill 0.631\hfill & \hfill 0.609\hfill & \hfill 0.796\hfill & \hfill 0.565\hfill & \hfill 0.722\hfill & \hfill 0.339\hfill & \hfill 0.415\hfill & \hfill 0.652\hfill & \hfill 0.581\hfill \end{array}\right] $$ - Step II:Calculating the normalized harmonic matrix (V)$$ \mathrm{V}={\mathrm{N}}_{\mathrm{DM}}\times {\mathrm{W}}_{\mathrm{T}}=\left[\begin{array}{ccccccccc}\hfill 0.0665\hfill & \hfill 0.0793\hfill & \hfill 0.0353\hfill & \hfill 0.0551\hfill & \hfill 0.0531\hfill & \hfill 0.0621\hfill & \hfill 0.1381\hfill & \hfill 0.0442\hfill & \hfill 0.0534\hfill \\ {}\hfill 0.0519\hfill & \hfill 0.0624\hfill & \hfill 0.0598\hfill & \hfill 0.0808\hfill & \hfill 0.0514\hfill & \hfill 0.0369\hfill & \hfill 0.1028\hfill & \hfill 0.0395\hfill & \hfill 0.0366\hfill \\ {}\hfill 0.0685\hfill & \hfill 0.0776\hfill & \hfill 0.0912\hfill & \hfill 0.0670\hfill & \hfill 0.0771\hfill & \hfill 0.0261\hfill & \hfill 0.0786\hfill & \hfill 0.0511\hfill & \hfill 0.0462\hfill \end{array}\right] $$
- Step III:Determining the positive ideal result and negative ideal result.$$ \begin{array}{c}\hfill \begin{array}{c}\hfill {A}^{+}=\left\{\left(\underset{i}{ \max }{v}_{ij}\left|j\in J\right.\right),\left(\underset{i}{ \min }{v}_{ij}\left|j\in J\prime \right.\right)\left|i=1,2,\dots, m\right.\right\}=\left\{\underset{i}{ \max }{v}_{i1},\underset{i}{ \max }{v}_{i2}, \min {v}_{i3},\underset{i}{ \min }{v}_{i4},\underset{i}{ \max }{v}_{i5}, \min v{}_{i6}, \max v{}_{i7}, \max v{}_{i8}, \min v{}_{i9}\right\}\hfill \\ {}\hfill =\left\{0.0685,\kern0.5em 0.0793,\kern0.5em 0.0353,\kern0.5em 0.0551,\kern0.5em 0.0771,\kern0.5em 0.0261,\kern0.5em 0.1381,\kern0.5em 0.0511,\kern0.5em 0.0366\right\}\hfill \end{array}\hfill \\ {}\hfill \begin{array}{c}\hfill {A}^{-}=\left\{\left(\underset{i}{ \min }{v}_{ij}\left|j\in J\right.\right),\left(\underset{i}{ \max }{v}_{ij}\left|j\in {J}^{\prime}\right.\right)\left|i=1,2,\dots, m\right.\right\}=\left\{\underset{i}{ \min }{v}_{i1},\underset{i}{ \min }{v}_{i2},\underset{i}{ \max }{v}_{i3},\underset{i}{ \max }{v}_{i4},\underset{i}{ \min }{v}_{i5}, \max v{}_{i6}, \min v{}_{i7}, \min v{}_{i8}, \max v{}_{i9}\right\}\hfill \\ {}\hfill =\left\{0.0519,\kern0.5em 0.0624,\kern0.5em 0.0912,\kern0.5em 0.0808,\kern0.5em 0.0514,\kern0.5em 0.0621,\kern0.5em 0.0786,\kern0.5em 0.0395,\kern0.5em 0.0534\right\}\hfill \end{array}\hfill \end{array} $$
- Step IV:Calculating the difference between each result and the positive and negative ideals$$ \begin{array}{c}\hfill \begin{array}{c}\hfill {d}_1^{+}=\sqrt{{\left(0.0665-0.0685\right)}^2+{\left(0.0793-0.0793\right)}^2+{\left(0.0353-0.0353\right)}^2+{\left(0.0551-0.0551\right)}^2+{\left(0.0531-0.0771\right)}^2+{\left(0.0621-0.0261\right)}^2+{\left(0.1381-0.1381\right)}^2+{\left(0.0442-0.0511\right)}^2+{\left(0.0534-0.0366\right)}^2}\hfill \\ {}\hfill {d}_1^{+}=0.025709\hfill \end{array}\hfill \\ {}\hfill \begin{array}{c}\hfill {\mathrm{d}}_1^{-}=\sqrt{{\left(0.0665-0.0519\right)}^2+{\left(0.0793-0.0624\right)}^2+{\left(0.0353-0.0912\right)}^2+{\left(0.0551-0.0808\right)}^2+{\left(0.0531-0.0514\right)}^2+{\left(0.0621-0.0621\right)}^2+{\left(0.1381-0.0786\right)}^2+{\left(0.0442-0.0395\right)}^2+{\left(0.0534-0.0534\right)}^2}\hfill \\ {}\hfill {\mathrm{d}}_1^{-}=0.069037\hfill \end{array}\hfill \end{array} $$The difference between each option and the positive and negative solutions in presented in Table 7Table 7
The difference for positive and negative ideal result

Alternative

Relative closeness to the right ideal solution

Relative closeness to the right ideal solution

Train

*d*_{1}^{+}= 0.025709d

_{1}^{−}= 0.069037Bus

d

_{2}^{+}= 0.051321*d*_{2}^{−}= 0.032903Van Shuttle

*d*_{3}^{+}= 0.060811*d*_{3}^{−}= 0.038327 - Step V:Determining the relative closeness (CL*) of each alternatives to the ideal result.$$ c{l}_{1^{+}}=\frac{d_{1^{-}}}{\left({d}_{1^{+}}+{d}_{1^{-}}\right)}=\frac{0.069037}{0.025709+0.069037}=0.728653 $$
- Step VI:
Prioritizing the Alternatives

Each option that has a bigger CL, would be selected. As it’s presented in Table 8, train transportation system with the highest relative closeness is the most suitable option. The next priority is bus and after that shuttle van is the best suitable alternative.Table 8Prioritizing the alternatives using TOPSIS method

Priority

Alternative

Relative closeness value

1

Train

0.728653

2

Bus

0.390661

3

Van shuttle

0.386606

## 5 Conclusion

The weight of effective parameters

Number | Criteria | Weight |
---|---|---|

1 | Safety | 0.18928 |

2 | Reliability | 0.12735 |

3 | Access time | 0.11851 |

4 | Access cost | 0.11459 |

5 | Easy access to system | 0.10862 |

6 | Comfort | 0.10689 |

7 | Time headway | 0.07951 |

8 | Interest to system | 0.07830 |

9 | Construction costs | 0.07690 |

At the end, by the weights of criteria and also inquiries that was made from transportation experts for determining the influence of each criteria on each transportation systems, the transportation modes were prioritized in TOPSIS method. Among suggested alternatives, train system was chosen by using TOPSIS method as the best and most suitable alternative.

## Declarations

**Open Access** This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

## Authors’ Affiliations

## References

- Alhussein S (2011) Analysis of ground access modes choice King Khaled International Airport, Riyadh, Saudi Arabia. J Transp Geogr 19:1361–1367View ArticleGoogle Scholar
- Harvey G (1986) Study of airport access mode choice. J Transp Eng 112(5):525–545View ArticleGoogle Scholar
- Pels E, Nijkamp P, Rietveld P (2001) Airport and airline choice in a multiple airport region: an empirical analysis for the san Francisco Bay Area. Reg Stud 35(1):1–9View ArticleGoogle Scholar
- Chebli H, Mahmassani HS (2003) Air travelers’ stated preferences towards new airport landside access mode services. Annual Meeting of Transportation Research Board, Washington DCGoogle Scholar
- Hess S (2004) A model for the joint analysis of airport, airline, and access-mode choice for passenger’s departing from the San Francisco Bay area. The European Transport Conference, StrasbourgGoogle Scholar
- Tam ML, Lam, William HK, Lo HP (2006) Modeling the effect of safety margin on air passenger behavior for ground access mode choice problems. In: 11th International Conference on Travel Behavior Research, KyotoGoogle Scholar
- Jou R, Hensher D, Hsu T (2011) Airport ground access mode choice behavior after the introduction of a new mode: a case study of Taoyuan International Airport in Taiwan. Transp Res E 47:371–381View ArticleGoogle Scholar
- Tsamboulas D, Evmorfopoulos AP, Moraiti P (2012) Modeling airport employees commuting mode choice. J Air Transp Manag 18:74–77View ArticleGoogle Scholar
- Meyer MD, Miller EJ (1984) Urban transportation planning, a decision oriented approachGoogle Scholar
- Fan Z, Hu G, Xiao S (2004) A method for multiple attribute decision - making with the fuzzy preference relation on alternatives. Comput Ind Eng 46:321–327View ArticleGoogle Scholar
- Gumus AT (2009) Evaluation of hazardous waste transportation firms by using a two-step fuzzy-AHP and TOPSIS methodology. Expert Syst Appl 36:4067–4074View ArticleGoogle Scholar
- Bashiri M, Badri H (2011) A group decision making procedure for fuzzy interactive linear assignment programming. Expert Syst Appl 38:5561–5568View ArticleGoogle Scholar
- Shelton J, Medina M (2010) Prioritizing transportation projects using an integrated multiple criteria decision - making method. TRB Annual Meeting CD-ROMGoogle Scholar
- Sadi-Nezhad S, Damghani K, Kaveh (2009) Application of a fuzzy TOPSIS method base on modified preference ratio and fuzzy distance measurement in assessment of traffic police centers performance. Appl Soft Comput 10:1028–1039View ArticleGoogle Scholar
- Zanjirchi M (2011) Fuzzy analytical hierarchy process. Saneie Shahmirzadi Press, IranGoogle Scholar