the prediction of passenger at carsamba airport

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Sep 2, 2016 - Prediction of the passengers are important because the landing capacity of a runway has been considered as the major constraint to futureΒ ...
INTERNATIONAL CONFERENCE ON ADVANCES IN SCIENCE ICAS 2016 31 AUGUST - 2 SEPTEMBER 2016, Istanbul, Turkey

THE PREDICTION OF PASSENGER AT CARSAMBA AIRPORT *Engin PEKEL Faculty of Machine, Department of Industrial Engineering, Yildiz Technical University Besiktas, Δ°stanbul, Turkey

Selin SONER KARA Faculty of Machine, Department of Industrial Engineering, Yildiz Technical University Besiktas, Δ°stanbul, Turkey

Keywords: Artificial neural network, genetic algorithm, intelligent water drops, forecasting * Corresponding author:, Phone:0212 383 27 49 E-mail address: [email protected]

order to provide a convenience in the determination of the air traffic and the fleet scheduling.

ABSTRACT Prediction of the passengers are important because the landing capacity of a runway has been considered as the major constraint to future expansion at many airports. The prediction of passenger literature is rich on the application of neural networks (NNs). This paper aims to illustrate the applicability of passenger demand prediction by using artificial neural network (ANN) in different algorithms. This study is applied to predict passenger demand through ANN by comparing genetic algorithm (GA) and intelligent water drops (IWD). The results for prediction method suggest that the only ANN provides a better forecasting performance than GA and IWD in terms of mean square error (MSE). Furthermore, this study illustrates that a successful prediction of the passenger is achieved b y using ANN.

An approach to analyze the demand for air passenger arrivals by using a neural network model, which incorporates timevarying conditional volatility, is proposed by researchers [15]. The neural network model predicts with three, five, ten and fifteen hidden neurons. The hybrid model, which is used in the study, is based on the Bayesian regularization. A number of researcher [16] tried to improve the model of grey neural networks to provide enterprises better prediction on market demand after transportation disruption. Some of researchers [17] proposed the technique of neural network forecasting as applied to the airline industry. They compared their neural network structure with the traditional forecasting techniques which are the moving average, the exponential smoothing, regression technique. These methods were compared on the basis of a standard error measure. The results of the neural network method provided better forecasts than the traditional forecasting methods.

INTRODUCTION The apron capacity of an airport has been considered as a huge trouble when the future expansion is considered. Nowadays, the raise of the air traffic loads forces airlines and airports to search for new borders in operations efficiency. In 2014, Airports Council International (ACI) reported over 1.8 billion passengers in domestic and international flights in Europe. Especially, airports are faced with continuing difficulties related to runway scheduling, a key bottleneck in the air transport system [1].

This study aims to illustrate the applicability of the proposed methods to predict the demand of the passenger. The result of this study suggests that the only ANN provides a better forecasting performance than GA-ANN and IWD-ANN in terms of mean square error (MSE). The rest of the paper is organized as follows: Section 2 presents the methodology that is consisted of the performance of GA ANN and IWD-ANN will be applied to predict passenger arrivals at the specified airport. Section3 explains the case study which is evaluated in this research. Section 4 presents the experimental results for the best prediction. Section 6 summarizes the overall findings.

The importance of prediction, based on ANN, cannot be disregarded in diverse fields such as transportation [2-4]. ANN applications in airport are generally based on the prediction of the inspection time [5], the prediction of the air travel and traffic demand [6-10], sound evaluation [11-13] and the prediction of airport energy consumption [14]. This paper presents a prediction of the passenger demand based on ANN in

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Conference Paper obstacles that limit the path establishment in their way to the destination, the real path has to be different from the ideal path and many twists and turns in the river path is observed [18].

METHODOLOGY

Consider an IWD in the layer 𝑖 and moves to the next layer 𝑗. The value of the weight on the arc between these two layers, 𝛽 represented by𝑀(𝑖,𝑗) , is used for updating the velocity π‘£π‘’π‘™π‘—πΌπ‘Šπ· of

Three different learning algorithms: Bayesian Regulation NN, hybrid GA-NN and IWD-NN are executed in order to predict the passenger demand. Four input parameters, which have 64 instances in each input and one output, are used to train these different NNs.

the IWD by:

π‘Žπ‘£

GA-ANN GAs are practiced to simulate some of the processes observed in natural evolution. GAs create a competitive set of solutions and these solutions are proceeded through the process of natural selection, where weak solutions run out and better solutions survive to reproduce. This process is iterated until the optimal solution is reached. Actually, a hybrid GA-ANN is a back propagation network with the only exception that the weight matrix is attained from performing the genetic operations under optimal convergence conditions. Various options such as population, fitness scaling, selection, reproduction, mutation, crossover and migration need to be specified as shown in Tab le 1.

π‘£π‘’π‘™π‘—πΌπ‘Šπ·

Options Creation Function Population size

Fitness scaling Selection Reproduction

Elite count Crossover fraction

M utation Crossover M igration Algorithm settings Hybrid function Stopping criteria

Hidden number Cpu time

neuron

Direction Fraction Interval Initial penalty Penalty factor Generations Time limit Fitness limit Stall generations Single hidden layer

+ {𝑏𝑣 + 𝑐𝑣 βˆ—

𝛽

𝛽 𝑀(𝑖,𝑗)

0

𝑖𝑓 𝑀(𝑖,𝑗) β‰ 

𝑏𝑣 𝑐𝑣

(1)

π‘œπ‘‘β„Žπ‘’π‘Ÿπ‘€π‘–π‘ π‘’

In Equation (1) π‘£π‘’π‘™π‘—πΌπ‘Šπ· represents the updated velocity of the IWD at the next layer 𝑗. Besides, π‘Žπ‘£ , 𝑏𝑣 , and 𝑐𝑣 are fixed velocity parameters that are set for the prediction. 𝑣𝑒𝑙 πΌπ‘Šπ· = π‘£π‘’π‘™π‘—πΌπ‘Šπ· + {

πœ€ 0

𝑖𝑓 |π‘£π‘’π‘™π‘—πΌπ‘Šπ· | < πœ€ π‘œπ‘‘β„Žπ‘’π‘Ÿπ‘€π‘–π‘ π‘’

(2)

where 𝑣𝑒𝑙 πΌπ‘Šπ· is reached by π‘£π‘’π‘™π‘—πΌπ‘Šπ· to keep its value away from zero with radius πœ€ in Equation (2). The fixed parameter πœ€ is a small positive value. Here, πœ€ = 0.001.

Table 1. Parameters of GA based ANN

Population

=

π‘£π‘’π‘™π‘—πΌπ‘Šπ·

Optimal conditions πΌπ‘Šπ· π‘‘π‘–π‘šπ‘’ (𝑖,𝑗) =

20 Stochastic uniform 2 0.55 Gaussian mutation Heuristic crossover Both 0.40

𝐷(𝑖, 𝑗) 𝑣𝑒𝑙 πΌπ‘Šπ·

(3)

Consider that a local distance function 𝐷(𝑖, 𝑗)is defined for a given problem to declare the distance of an IW D to move from one layer to another. The time, which is taken for an IWD, has the velocity π‘£π‘’π‘™π‘—πΌπ‘Šπ· to move from the current layer 𝑖 to its next πΌπ‘Šπ· layer 𝑗, denoted by π‘‘π‘–π‘šπ‘’ (𝑖,𝑗) in Equation (3). βˆ†π‘€ (𝑖,𝑗) =

10 100 1000 1.00e-8 50 6

π‘Žπ‘  πΌπ‘Šπ· 𝑏𝑠 + 𝑐𝑠 βˆ— π‘‘π‘–π‘šπ‘’ (𝑖,𝑗)

(4)

βˆ†π‘€ (𝑖,𝑗) is the weight that the IWD , which has velocity 𝑣𝑒𝑙 πΌπ‘Šπ· , removes from the synapse between layer 𝑖 and 𝑗 in Equation (4).π‘Žπ‘  , 𝑏𝑠 , and 𝑐𝑠 are constant velocity parameters that their πΌπ‘Šπ· values depend on given problem. The value π‘‘π‘–π‘šπ‘’ (𝑖,𝑗) is defined in Equation (3) and it represents the time that flows from 𝑖to 𝑗.

108.25

𝑀 (𝑖,𝑗) = πœŒπ‘œ βˆ— 𝑀 (𝑖,𝑗) βˆ’ πœŒπ‘› βˆ— βˆ†π‘€ (𝑖,𝑗)

IWD-ANN

(5)

After an IWD moves from layer 𝑖 to layer 𝑗, the weight 𝑀 (𝑖,𝑗) on the synapse between the two layers is reduced as in Equation (5). πœŒπ‘œ and πœŒπ‘› are positive numbers that are chosen between zero and one.

Water drops, which flow in rivers, lakes, and seas, are the origin of inspiration of IWD. This situation may be more obvious in rivers, which reach the destination to lakes, seas, or oceans, in spite of many different kinds of obstacles on their ways. In the water drops of a river, the gravitational force of the earth assures the tendency for flowing toward the destination. If there is no obstacles, the water drops follow an uncurving path toward the destination that is the shortest path from the source to the destination. However, owing to distinct kinds of

𝑀 (𝑗,π‘˜) = 𝑀 (𝑗,π‘˜) +

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βˆ†π‘€ (𝑖,𝑗) 𝑛 βˆ—π‘–

(6)

Conference Paper The IWD, which is moved from layer 𝑖 to 𝑗, increases the

EXPERIMENTAL RESULTS

βˆ†π‘€(𝑖,𝑗)

weight 𝑀 (𝑗,π‘˜) as in in Equation (6). Here, 𝑛 is the hidden π‘›βˆ—π‘– neuron number and 𝑖 is the input parameter number. All static parameters for IWD are given in Table 2.

Two hybrid methods, GA-ANN and IWD-ANN, and Bayesian ANN, are experimented to predict the passenger demand.

Table 2. Parameters of IWD based ANN

π‘Žπ‘£ π‘Žπ‘  𝑏𝑣 𝑏𝑠 𝑐𝑣 𝑐𝑠 𝛽 π‘€π‘Žπ‘₯_π‘–π‘‘π‘’π‘Ÿ Mc Hidden neuron number Hidden layer

Optimal conditions 1 1 0.01 0.01 1 1 2 400 0.25 6 Single hidden layer

Options 𝐼𝑛𝑖𝑑_𝑣𝑒𝑙 HUD(i, j) πœŒπ‘œ πœŒπ‘› n 𝑖 πœ€ 𝐼𝑛𝑖𝑑_π‘šπ‘ π‘’ Transfer function Cpu time

Optimal conditions 5 100 0.9 0.1 5 4 0.001 10 Sigmoidal

0,400 MSE

Options

IWD_ANN 0,000

ANN 5 6 7 8 9 10 11 12 13 14 15 Number of neuron

51.67 Figure 1. Graphical representation of MSE values for three different ANN types

Bayesian ANN provides the least MSE values except five neurons and GA-ANN provides a less MSE than IWD-ANN in Figure 1. Although IWD-ANN does not offer the least and reasonable MSE, it predicts faster than other algorithms that include Bayesian ANN and GA-ANN. This consequence is seen in the part of cpu time in Table 1, Table 2 and Table 3.

ANN ANN is practiced to simulate the basic biological neural systems in the human brain. A couple of nodes , which are interconnected, exist in ANNs. An input signal is collected by each node and it is processed locally through an activation or transfer function. Finally, a transformed output signal is generated. Though each function is implemented by each individual neuron quite slowly, a network may execute an amazing number of tas ks efficiently [19-20]. Multi-layer feed forward ANN, which provides the best result, is illustrated in Table 3.

1,000 R Square

0,800

Table 3. Parameters of ANN

Parameters Hidden neuron number Hidden layer Training method Learning rate Transfer functions Performance M aximum iteration Cpu time

GA_ANN

0,200

Optimal conditions 8 Single hidden layer Bayesian Regulation 0.05 Sigmoidal M ean square error 1000 122

0,600

GA_ANN

0,400

IWD_ANN

0,200

ANN

0,000 5 6 7 8 9 10 11 12 13 14 15 Number of neuron Figure 2. Graphical representation of 𝑅2 values for three different ANN types

CASE STUDY

Bayesian ANN provides the best 𝑅 2 values at each level of number of neuron and IWD-ANN provides a better 𝑅 2 than GAANN except nine, eleven, twelve and fifteen neurons in Figure 2. Although IWD-ANN does not offer the best 𝑅 2 , it predicts faster than other algorithms that include Bayesian ANN and GA-ANN. Moreover, IWD-ANN provides a reasonable 𝑅 2 values.

Samsun, where has over half a million people on the north coast, in Turkey, is considered as the capital the Black Sea region. The growing city has two universities, many hospitals and light manufacturing industry. Carsamba airport is a public airport, which is away 23 km from Samsun. The data used in this study is gathered from General Directorate of State Airports Authority (D.H.M.I) in 2015. The acquired data belongs to number of passenger who arrives and departs.

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Conference Paper [5]. Ruiz-Aguilar, J. J., T urias, I. J., & JimΓ©nez-Come, M. J. (2014) . Hybr id approaches based on SARIMA and artificial neural networks for inspection time series forecasting. Transportation Research Part E: Logistics and Transportation Review, 67, 1-13. [6]. Al-Rukaibi, F., & Al-Mutairi, N. (2013). Forecasting air travel demand of Kuwait: A comparison study by using regression vs. artificial intelligence. Journal of Engineering Research, 1(1), 113-143. [7]. Chen, M. Q. (2013, September). Using Hybrid Fuzzy Neural Networ k t o Improve the Accuracy of Air T raffic Flow Fo recasts. In Applied Mechanics and Materials (Vol. 333, pp. 1422-1425). [8]. Xie, G., Wang, S., & Lai, K. K. (2014). Short -term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches. Journal of Air Transport Management, 37, 20-26. [9]. Xiao, Y., Liu, Y., Liu, J. J., Xiao, J., & Hu, Y. (2016). Oscillations extracting for the management of passenger flows in the airport of Hon g Kong. Transportmetrica A: Transport Science, 12(1), 65-79. [10]. Chen, M. Q., & Feng, J. H. (2013, March). Research of Air Traffic Flo w Forecasts Based on BP Neural Network. In Advanced Materials Research (Vol. 671, pp. 2912-2915). Trans Tech Publications. [11]. Lin, M. D., T sai, K. T ., & Su, B. S. (2009). Estimating the sound absorption coefficients of perforated wooden panels by usin g a r t if ic ia l neural networks. Applied Acoustics, 70(1), 31-40. [12]. Yang, Y., Gillingwater, D., & Hinde, C. (2005). Applying neural networks and geographical information systems to airport noise evaluation. In Advances in Neural Networks–ISNN 2005 (pp. 998-100 3) . Springer Berlin Heidelberg. [13]. Cerqueira Revoredo, T., Ghislain Slama, J., & Mora Camino, F. ( 2 01 5) . Neural Prediction of Aircraft Noise Levels Along a Flight T rajectory. Latin America Transactions, IEEE (Revist a I EE E A me ric a Latina), 13(5), 1313-1320. [14]. Chen, J. J., Xiao, C., & Qian, W. G. (2012, Decem ber) . Pr edic tio n o f Airport Energy Consumption Using a Hybrid Grey Neural Network Model. In Advanced Materials Research (Vol. 608, pp. 1252-1256). Trans T ech Publications. [15]. Medeiros, M. C., McAleer, M., Slottje, D., Ramos, V., & Rey -Maquieira, J. (2008). An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequenc y a ir p a sse n ge r arrivals. Journal of Econometrics, 147(2), 372-383. [16]. Liu, C., Shu, T ., Chen, S., Wang, S., Lai, K. K., &Gan, L. (2016). An improved grey neural network model for predicting transportation disruptions. Expert Systems with Applications, 45, 331-340. [17]. Weatherford, L. R., Gentry, T. W., &Wilamowski, B. ( 2 003 ). Ne ur a l network forecasting for airlines: A comparative analysis. Journal of Revenue and Pricing Management, 1(4), 319-331. [18]. Shah-Hosseini, H., 2008. Intelligent water drops algorithm: A new optimization method for solving the multiple knapsack problem. International Journal of Intelligent Computing and Cybernetics, 1(2), 193-212. [19]. Reilly, D. L., & Cooper, L. N., 1990, January. An o ve rvie w o f n e ur a l networks: early models to real world systems. In An introduction to neural and electronic networks (pp. 227-248). Academic Press Professional, Inc. [20]. Zhang, G., Patuwo, B. E., & Hu, M. Y., 1998. Forecasting with artific ial neural networks:: T he state of the art. International journal of forecasting, 14(1), 35-62.

CONCLUSION The prediction of the passenger demand is important to establish more accurate fleet scheduling. The prominence of passenger demand prediction for Carsamba airport cannot be disregarded since it has an important flight which is more than 10K a year. This study illustrates that a successful prediction of the passenger demand is achieved by using ANN. GA, IWD and Bayesian Regulation ANN are compared with regard to MSE and 𝑅 2 to predict passenger demand. The findings of this paper are summarized in the following. 1.

Bayesian ANN provides the least MSE values except five neurons and GA-ANN provides a less MSE than IWD-ANN in Figure 2. Although IWD-ANN does not offer the least and reasonable MSE, it predicts faster than other algorithms that include Bayesian ANN and GA-ANN. This consequence is seen in the part of cpu time in Table 1, Table 2 and Table 3.

2.

Bayesian ANN provides the best 𝑅 2 values at each level of number of neuron and IWD-ANN provides a better 𝑅 2 than GA-ANN except nine, eleven, twelve and fifteen neurons in Figure 2. Although IWD-ANN does not offer the best 𝑅 2 , it predicts faster than other algorithms that include Bayesian ANN and GA-ANN. Moreover, IWD-ANN provides a reasonable 𝑅 2 values.

REFERENCES

[1]. Farhadi, F., Ghoniem, A., & Al-Salem, M. (2014). Runway capacity management–an empirical study with application to Doha Inter nation al Airport. Transportation Research Part E: Logistics and T r ansp ortation Review, 68, 53-63. [2]. Barros, C. P., & Wanke, P. (2015). An analysis of African airlines efficiency with two-stage T OPSIS and neural networks. Jo ur nal o f Air T ransport Management, 44, 90-102. [3]. Alekseev, K. P. G., & Seixas, J. M. (2009). A multivariate neural forecasting modeling for air transport–Preprocessed by decomposition: A Brazilian application. Journal of Air T ransport Management, 15(5), 2 12 216. [4]. Xie, G., Wang, S., & Lai, K. K. (2014). Short -term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches. Journal of Air T ransport Management, 37, 20-26.

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