Airline Demand Forecast

STIMATION OF AIR TRAVEL DEMAND IN flop ENAR TUNC, Orhan sIvrIkaya* Okan UNIVERSITY Title ESTIMATION OF AIR TRAVEL DEMAND IN bomb Orhan Sivrikaya*(Candidate Phd. ), OKAN UNIVERSITY Tel 0-532-4265392 Fax 0-212-4652299 Email emailprotected com Enar Tunc, Professor of Industrial Engineering, OKAN UNIVERSITY Keywords * national cinch acid, urban center P institutionalize, broth and Destination, pick up, Forecast, Gravity get, Multivariate Regression and Detour Factor. Total rogue 11 Abstract true statement in estimating melody passage merchandise involve is a hear element while an mannerline is planning its short term or long term business plan regardless of its status quo creation an incumbent or startup comp all. Turkish domestic market of behavior propel industry has been dramatic altogethery grown in recent old age especi each(prenominal)y after the deregulation commencing on the renewal of telephone circuit battery-acid policy in 2003. However there is non an y relevant scientific research in the literature to analyze the determining eventors on transport travel pack of domestic city pairs in Turkey.A variable regression mannikin is falld in order to fit the air travel involve in yield of riders carried. The typeing is based on accumulate individual market which consists of on-line city pairs. The homunculus is prepare of importly representative within the experimental info bulge of the years 2008 and 2009 including the ascendant and destination pairs for 40 on-line cities. Then, the mold is tested by victimisation 2010 common fig trees in order to comp atomic number 18 expectation apprises with tangible figures. Accuracy aim is found to be encouraging for potential new dromes or potential new r step to the forees to be evaluated by using the model estimates. . portal The deregulation of air loony toons market in Turkey in 2003 has started revolutionary changes in the skyway industry. New government having th e target to cast up the portion of air travel stunned of all modes of local transferation attempted to encourage much airline companies to enter the market and alter them to offer more attractive prices by tax cutting especial(a) to the airline sector. Price oriented arguing has worked very tumefy to gene compute signifi so-and-sot airline passenger commerce.Low Cost Carriers have contri plainlyed to exercise a sustainable devil digit growth by stealing passenger employment from bus expatriate as a result of shorten the gap amidst relative prices. Turkish air passages as a legacy carrier has responded to structural changes in the market by applying self-propelled determine policy and growth strategy to benefit from economies of scale resulting in make up in productivity. Big changes in airline passenger traffic in Turkey create a challenge to testify any claiming model built to estimate air travel adopt. Macroeconomic or demographic changes do not seem to be responsible for whole make headway in air travel have.Competition doubled or tripled available stinker capacity on well-nigh routes so that it was required a various strategy to generate additional demand to achieve in capable load factor which is a key performance indicator for airline profitability. stemmaline traffic is most of the time considered as a signifi potfult indicator for the performance of the nations entire industry since it is extremely correlated with the number of business events and interactions with different industries simultaneously. So, it implies that changes in economies may diverge airlines traffic indirectly.However, airline specific parameters like tatter price and degrees of competition ar also supposed to be main driver for passenger demand besides the macroeconomic factors. The sustainable success of any organisation or company is rigorously related with how well management or decision makers are able to foresee the future and develop c aptivate strategies. The objective of this conduct is to examine the demand size of it for air transport in Turkey and show its implications for air transport planning. 2. BACKGROUND It has been seen passim the results of the previous research in the iterature that one of the most important emerge to develop a predictive model is to favour the right crew of the variables which represent the determining factors involved in the model. These variables are categorized by two subgroups (Carson et al. 2010) 1. Geo-economics Factors which consist of geographical characteristics, economical activities, social factor etc. 2. Service Related Factors which are related to airline dependent factors. The other prominent aspect of model generation is the level of forecast which rout out be classified by two groups as well 1.Microscopic type commitport specific or city pair specific data is involved much(prenominal) that it refers the total number of incoming and outgoing passengers per special airport or per city pair. 2. Macroscopic Model Region or country specific data is involved such that it refers to gatherd number of passengers in a region or country regardless of stock or destination city. conflate Individual planetary houseet (AIM) forecast outperforms the aggregate approach since the forecasting power gained by exploiting heterogeneous information coddlewise markets dominates the forecasting power lost due to estimation of many coefficients (Carson et al. 2010).Local field of force information appears to be more relevant in determining local O& ampereD travel than of national information such as gross domestic product (Bhadra 2003). - 3. OVERVIEW OF THE determ? nants for air passenger demand ? n turkey Turkey is spread over a encompassing geographical area and road ways are not adequately constructed for all direction. Hence, air transportation is supposed to have more shares out of total statistics in domestic transportation covering all thinkab le city pairs. While the gap amid relative prices is being shortened, more and more nation find it affordable to fly sheet.This study is aiming to find out the determining factors which are concerned to turn potential demand into air travel passengers. The proposed model is not only to explain actual traffic results but also to estimate potential traffic between cross cities which are not connected directly or to evaluate off-line cities to build new airport. Population, gross domestic product per capita and employment rate are considered as the leading macroeconomic dynamics behind air travel demand as depicted in the confuse 1. total fare has a stimulating effect on airline demand as Brons et al. 2002) pointed out that ticket price is an elastic driver for airline demand generation. in that respect are also specific indicators for a particular city pair traffic representing interactivity between the concerning cities such as place and number of migrants from all(prenominal ) other. The number of bus registered in a city is indicating the volume of bus transportation which is considered to be negatively related with air travel demand. Since number of carriers as a degree of competition contributes to market expansion, it is also embed in the model expecting a positive tattle with air travel demand. sidestep 1 Commonality in Types of Variables Variables Name Percentage of Occurrence* GDP 50. 0 % GDP per Capita 35. 7 % Unemployment Rate 14. 3 % Fuel Price 7. 1 % Number of Employees 7. 1 % Population 42. 8 % medium serve 57. 1 % CPI 14. 3 % Trade per Capita 14. 3 % Exchange Rate 14. 3 % Service frequence 28. 6 % Distance 42. 8 % Expenditures 7. 1 % * The percentages are calculated out of a sample of 14 different relevant articles. Most of the itineraries between city pairs are not directly connected that means air passengers travel with connecting flights via one or more transfer points.If there is no direct usefulness the dummy variable transit get s 1 and 0 otherwise. Naturally, passengers would not prefer to fly with connecting flights so it is anticipated to be negatively partakeing air travel demand. 4. ECONOMETRIC ESTIMATION data, Methodology and results information availability is main output when data coverage is decided. Experimental model is based on the data of the two years 2008 and 2009 since all informative variables are available within the specified flow. There are 40 on-line destinations in domestic network in Turkey.This number of destinations can theoretically generate 1560 different origin and destinations (O&Ds) on which direct or connecting flights are possible. However experimental sample does not cover data for all possible on-line O&Ds because some city pairs which are at close distance are not meaningful to fly with connecting flights or the concerning flights are not connected each other. There are 231 city pairs which are served with direct flights, whereas the remaining city pairs are fou nd to be f first gearn by connecting flights via an appropriate domestic hub.Under the assumption of approximately the same number of O&Ds for each year, data size will be duplicated for the two years period. argumentport statistics for all plan carriers are used in the experimental model as a source of the dependent variable. Transfer traffic is removed from the statistics for each city pair, since the proposed model is to estimate pure O&D passenger by using data specific to the corresponding city pairs. Average prices for each city pair are estimated by using airlines weathervane site. Road distance between the cities is taken from the web site of the customary Directorate Highways of Turkey.Population of the cities, GDP per capita of the cities, the number of migrants between the cities, the number of bus registered in the citys account and labour rate of the cities are obtained from the chest of Statistics in Turkey. Weighted average of the corresponding citys race is used, while GDP per capita and the labour rate are being reborn to O&D level. A variety of different models experience for passenger volume estimation. Since no single model guarantees accuracy, airlines in fact compare forecasts from several different models.Within this set of forecasting methods, the most demand models used are of the simple gravitational force type formulation. (S. C. Wirasinghe et al. 1998). The gravity model for the estimation of domestic passenger volume between city-pairs is examined in this study. By excluding unavailable inspection and repair-related or market specific input variables, and using cross-section(a) calibration data, the model is particularly applicable to city-pairs where no air returns exists, historical data is unavailable, or factors describing the current service level of air transportation are not available.Average price for city-pairs with no air service is estimated by fall back mechanism that it uses the average price which is normalized by distance of the cities having similar market structure. All other explanatory variables are not service related factors and available for the city-pairs with no air service. The gravity model takes the form D=?. AaBbCc This model assumes that the marginal effects of each variable on demand are not constant but depend on both the value of the variable and the values of all other variables in the demand function (Aderamo 2010).In other words, the explanatory variables affect demand in multiplicative manner. Partial derivation of any free-living variable proves aforementioned relationship. However, this model can be made competent for multiple regressions by applying logarithmic transformation. Logarithmic form of the gravity model takes the form LogD=? 0 + ? 1LogA + ? 2LogB + ? 3LogC + where ? 0=Log? It is obvious that interdependency is intractable in this form so that multiple regression model can be applied. The proposed multiple regression model is generated by using SAS Jmp 9 tool.Table 2 shows the matrix of correlation between the commutative variables. The results show that some of the variables are interrelated. For example, Log_Migrant has a correlation coefficient of 0. 8661 and 0. 8150 with Log_Pop* and Log_Bus* respectively. Where both Log_Migrant* and Log_Pop* are calculated by taking the product of population of origin and destination cities. However, omitting any of these two variables would red-bloodedly reduce the model fit. As the goal is to obtain a trusty estimation of the passenger volume, all interrelated variables were included (Grosche et al. 007). Furthermore, it has been verbalise that if the sole purpose of regression analysis is prediction or forecasting, and so multicollinearity is not a serious problem because the higher R2, the better prediction(R. C. Geary, 1963). In order to verify stepwise regression fit of the model, stepwise serve up by backward direction and minimum AICc selection is used. When all i ndependent variables as depicted in Table 2 are entered, the smallest AICc value 2665. 913 is found. Adjusted R2 as shown in the Table 3 is 0. 823991 which is fairly good.In the Table 4, adjusted R2s are compared including the relevant articles in the reference list. This proportion table shows that the studied model efficiency is relatively successful. As shown in the table 5, the F test also shows that the regression is significant since F statistic of 497. 2411 is obviously higher than the critical value of 2. 32 at 0. 01 level of significance. In the table 6, parameter estimates are depicted. As seen in the table, all independent variables are significant at 0. 01 level of two apparition significance considering their t-statistics.Since the coefficients of the regression model represent elasticities of the corresponding variables, how change of any variable affects demand estimation can be determined. The price ginger nut of passenger demand is approximately -1. 1 which impli es that airline passenger demand in Turkey is elastic. This finding is compliant with the fact that after low cost carriers entered into the market by lowering ticket prices, market size has been tramendously enlarged. Domestic passenger traffic grows higher than the decreasing rate of ticket price.Both GDP per capita and ticket price seem to have elastic cushion on passenger demand estimation. Air transportation and bus transportation seem to be competing each other because of their negative relation. When air service is provided by connecting flight which means transit traffic, air transport demand is decreasing. This result is not surprising because people do prefer to fly directly. Another result is that the number of airlines participating in each O&D market tends to have a positive tinge on the number of passengers traveled between O&D pairs, perhaps representing the ffects of choice more than anything else. Lastly, distance and the number of migrants are found positiv ely related with air transport demand as expected. Table 4 Model Efficiency Benchmark Research Name take aim of Forecast Author Year Independent Variables Observation Adjusted R Square Demand For Air Transport In Nigeria Aggregate Adekunle J. Aderamo 2010 advocator of AgricultureIndex of ElectricityGDP 23 0. 923 Air croak Domestic Demand Model in Bangladesh Aggregate Md. Jobair lay in Alam Dewan Masud KArim 1998 PopulationGDPDistance 31 0. 8 An Econometric Analysis of Air Travel Demand in Saudi Arabia Aggregate Seraj Y. Abed Abdullah O. Ba-FailSajjad M. Jasimuddin 2001 PopulationTotal Expenditures 25 0. 959 Regression Model for rider Demand A case study of Cairo Airport Aggregate Dr. Khaled A. Abbas 2003 Population GDPForeign Tourist 88 0. 82 Demand for Airravel In USA O&D Dipasis Bhadra 2003 Density, Interaction, Distance, Marketshare, Fare 2424 0. 57 An Aggregate Demand Model in Hub-and-Spoke Aggregate Wenbin WeiMark Hansen 2006 Frequency, Number of Spokes, Fare, Distance, Capacity, Traffic Type 897 0. 92 Gravity Model for Airline rider Volume Estimation City-pairs Tobias GroscheFranz RothlaufArmin Heinzl 2007 DistancePopulationCatchment Area 956 0. 761 The number of migrants indicates the relationship between city-pairs hence it positively affects on point to point air traffic demand. When distance is greater, air transport demand increases due to the fact that people get higher utility comparing to the alternative modes of transportation. In the figure 1, model fit of the experimental data is shown in scatter diagram. There are total 955 observations within experimental data.A test data is obtained from 2010 actual results which consists of 562 observations. The model predicts 2010 figures with a Mape (Mean Absolute Percentage Error) value 14. 1 %. true data of 2010 is refined by excluding the O&Ds having less than 104 yearly passengers flow and detour factors smaller than 3. Logic of this filtering is to choose meaningful connections out of the all itineraries. Although the model is performing significantly well with a relatively high Rsquare value, small discrepancy in prediction value may result in larger inaccuracy in passenger demand estimate because of logarithmic aspect of the regression. . CONCLUSION This study demonstrate that the proposed econometric estimation and using micro data based on local area information can result in substantial insights to O&D travel. The demand model reveals all the quantifiable relationships among the used variables, which is helpful for airlines to understand the consequence of change of their decision variables or adjustment of their routing structures, and also useful for the related authority to quantify the benefits of airport capacity expansion and to take into account while airport structure plan is being evaluated.It would be advantageous to extend the time period covered by the analysis. This would enable to examine possible differences in snap amongst city-pairs. Ext ending the data back in time would also provide observations of airfares progress. The model efficiency may be improved for even more reliable estimation, if more independent variables indicating bilateral relations between city-pairs are embedded in the model such as the number of call between city-pairs or credit card statistics of domestic visitors. References S. C. Wirasinghe and A. S. Kumarage, An Aggregate Demand Model for Intercity rider Travel in Sri Lanka. menu 25 77-98, 1998. R. C. Geary, Some Results about relations between Stochastic Variables A Discussion Document, Review of internationalistic statistical Institute, Vol. 31, pp. 163-181, 1963. Richard T. Carson, Tolga Cenesizoglu and Roger Parker. Aggregate Demand for USA Commercial Air Travel. Department of Economics, University of California. 2010. Elton Fernandes and Ricardo Rodrigues Pacheco. Air Transportation Analysis Passenger Demand in Brazil. Aerlines Magazine e-zine edition, issue 33. Adakunle J. Aderamo. Dem and for Air Transport in Nigeria. daybook of Economics, 1 (1) 23-31 (2010).Md. Jobair Bin Alem and Dewan Masud Karim. Air Travel Demand Model For Domestic Air Transportation in Bangladesh. Journal of Civil Engineering The Institution of Engineers, Bangladesh Vol. CE 26, nary(prenominal) 1, 1998. Seraj Y. Abed, Abdullah O. Ba-Fail and Sajjad M. Jasimuddin. An Econometric Analysis of International Air Travel Demand in Saudi Arabia. Journal of Air Transport Management 7 (2001) 143-148. Abdullah O. Ba-Fail and Seraj Y. Abed. The Determinants of Domestic Air Travel Demand in the Kingdom of Saudi Arabia. Journal of Air Transportation World Wide Vol. 5, No. 2 2000. Abdullah Omer Ba-Fail.Applying info Mining Techniques To Forecast Number of Airline Passengers In Saudi Arabia (Domestic and International Travels). Aviation Institute, University of Nabraska at Omaha, 2004. Senanu Ashiabor, Hojong Baik and Antonio Trani. Logit Models For prognostic Nationwide Intercity Travel Demand in the USA. Journal of the Transportation Research Board, No. 2007, Washington, D. C. , pp. 1-12 2007. J. Scott Armstrong and Michael C. Grohman. A Comparative report of Methods for Long-Range Market Forecasting. Management Science, 19 (2), pp. 211-221 1972. Khaled A. Abbas. Conceptual and Regression Models for Passenger Demand Prediction.Aerlines Magazine e-zine edition, issue 26 2003. Dipasis Bhadra. Demand for Air Travel in the United States Bottom-up Econometric Estimation and Implications for Forecasts by Origin and Destination Pairs. Journal of Air Transportation Vol. 8, No. 2 2003. Radosav B. Jovanovic. talk Intrinsic Characteristics as a Forecasting Tool A dynamic perpective. Second International Conference on Research in Air Transportation Belgrade, June 2428 2006. Martijn Brons, Eric Pels, Peter Nijkamp and Piet Rietveld. Price Elasticities of Demand for Passenger Air Travel. Journal of Air Transport Management 8 pp. 65-175 2002. Dail Umamil Asri and Yoriyasu Sugie. coincidi ng Demand Model for Passenger Travel. Proceedings of the eastern Asia Society for Transportation Studies, Vol. 4, October, 2003. Joyce Dargay and Mark Hanly. The Determinants of the Demand for International Air Travel to and from UK. ESRC Transport Studies Unit, midriff for Transport Studies, University College London, November 2001. Catherine Zhukovskaya. Use of the Generalized Linear Model in Forecasting the Air Passengers Conveyances from EU Countries. Computer Modelling and Technologies, Vol. 11, No. 1, pp. 6272, 2007. Wenbin Wei and Mark Hansen.An Aggregate Demand Model for Air Passenger Traffic in the Hub and Spoke Network. Transportation Research Part A 40 pp. 841851, 2006. Matthew G. Karlaftis. Demand Forecasting in Regional Airports. Straer 7 pp. 100-111, Tr. 312, 2008. Tobias Grosche, Franz Rothlauf and Armin Heinzl. Gravity Models for Airline Passenger Volume Estimation. Journal of Air Transport Management 13 pp. 175-183, 2007. Chaug-Ing Hsu and Su-Miao Liu. Predicting City-Pair Air Passenger Traffic Using Grey Topological Forecasting Model. Journal of the Eastern Asia Society for Transportation Studies, Vol. 5, October, 2003.