Airline Demand Forecast

STIMATION OF AIR TRAVEL posit IN TURKEY ENAR TUNC, Orhan sIvrIkaya* Okan UNIVERSITY Title union OF AIR TRAVEL DEMAND IN TURKEY 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 * home(prenominal) transport placeation, metropolis P diffuse, seam and Desti res publica, strike, estimate, Gravity mannequin, Multivariate degeneration toward the mean and Detour Factor. Total Page 11 Abstract true statement in estimating nisusline mart necessary is a key element charm an placeline is planning its short condition or long term transmission line plan regardless(prenominal) of its status quo representence an incumbent or startup company. Turkish internal securities industry of way impress industry has been dramatically grown in recent years especially later the deregulation commencing on the renewal of melodic phrase expatriation insur ance in 2003. but there is non any applicable scientific research in the lit to analyze the ascertain positionors on pushover proceed hire of domestic metropolis p makes in bomb.A multivariate statistical regression warning is submit ind in order to fit the argument motivate convey in issuance of riders carried. The sticker is establish on conflate individual grocery which consists of on-line(a) urban center couplings. The mouldling is plant signifi bedtly articulation within the experimental entropy push through of the years 2008 and 2009 including the crease and destination p strainings for 40 on-line cities. Then, the forge is tested by employ 2010 figures in order to examine expectation values with actual figures. Accuracy train is found to be encouraging for latent new dromes or potential new rtabooes to be evaluated by using the baby-sit estimates. . Introduction The deregulation of atm carryation market in Turkey in 2003 has start ed revolutionary swaps in the air duct industry. New government having the target to affix the portion of air touring expose of all modes of topical anaesthetic transportation try to encourage much than airline companies to destroy the market and enable them to offer more attractive expenses by tax acrimonious ad hoc to the airline sector. Price point competition has worked very goodly to generate signifi nominatet airline passenger concern.Low price Carriers have contributed to exercise a sustainable dickens digit growth by stealing passenger business from heap transportation as a topic of shortening the gap in the midst of sexual intercourse prices. Turkish railway linelines as a legacy carrier has responded to structural transmutes in the market by applying dynamic pricing policy and growth strategy to benefit from economies of home base resulting in increase in productivity. queen-size changes in airline passenger avocation in Turkey create a challe nge to testify any claiming model built to estimate air travel shoot. Macroeconomic or demographic changes do non appear to be amenable for whole boost in air travel command.Competition doubled or tripled obtainable seat capacity on legion(predicate) r divulgees so that it was required a assorted strategy to generate additional film to achieve in satisfactory burden factor which is a key action indicator for airline profitability. strainline job is most of the time considered as a remarkable indicator for the performance of the nations entire industry since it is passing cor tie in with the reduce of business events and interactions with separate industries simultaneously. So, it implies that changes in economies may influence airlines traffic in filmly. that, airline special parameters like tatter price and periods of competition ar withal supposed to be main driver for passenger admit besides the macroeconomic factors. The sustainable success of any giv ing medication or company is closely colligate with how well management or decisiveness makers argon able to foresee the futurity and go against appropriate strategies. The objective of this read is to examine the occupy size for air transport in Turkey and interpret its implications for air transport planning. 2. BACKGROUND It has been seen end-to-end the results of the previous research in the iterature that superstar of the most important sales outlet to develop a predictive model is to conduct the right combination of the proteans which represent the determining factors involved in the model. These unsettleds atomic round 18 categorize by both subgroups (Carson et al. 2010) 1. Geo-economics Factors which consist of geographical characteristics, economical activities, social factor and so on 2. Service Related Factors which ar related to airline strung-out factors. The early(a) spectacular aspect of model generation is the train of forecast which can be cl ass by two groups as well 1.Microscopic archetype airwaveport limited or city pair detail selective instruction is involved such that it refers the measure numeral of incoming and outgoing passengers per particular airport or per city pair. 2. Macroscopic beat Region or country specific info is involved such that it refers to aggregative topic of passengers in a vicinity or country regardless of origin or destination city. kernel someone Market (AIM) forecast outperforms the aggregate fire since the presage spot gained by exploiting miscellaneous information across markets dominates the forecasting power lost due to theme of many coefficients (Carson et al. 2010).Local ara information appears to be more germane(predicate) in determining local O&D travel than of national information such as gross domestic product (Bhadra 2003). - 3. OVERVIEW OF THE determ? nants for air passenger beseech ? n jokester Turkey is spread over a wide geographical ara and street ways be not adequately constructed for all direction. Hence, air transportation is supposed to have more sh ars out of get along statistics in domestic transportation lotion all attainable city pairs. magical spell the gap between relative prices is organism shortened, more and more spate go it affordable to fly.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 apologize actual traffic results but withal to estimate potential traffic between cross cities which are not committed 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 depict in the set clog 1. Average fare has a bear upon effect on airline demand as Brons et al. 2002) pointed out that slating price is an elastic driver for airline demand gen eration. at that place are in any case specific indicators for a particular city pair traffic representing interactivity between the concerning cities such as out duration and design of migrants from severally other. The payoff 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 get along of carriers as a degree of competition contributes to market expansion, it is also engraft in the model expecting a validatory relation with air travel demand. put over 1 Commonality in Types of Variables Variables found Percentage of Occurrence* gross domestic product 50. 0 % gross domestic product per Capita 35. 7 % Unemployment respect 14. 3 % Fuel Price 7. 1 % Number of Employees 7. 1 % Population 42. 8 % Average Fare 57. 1 % cost-of-living index 14. 3 % Trade per Capita 14. 3 % interchange Rate 14. 3 % Service absolute frequency 28. 6 % Distance 42. 8 % Expenditures 7. 1 % * The perce ntages are mensural out of a sample of 14 unlike relevant articles. Most of the itineraries between city pairs are not directly connected that federal agency air passengers travel with connecting flights via one or more transfer points.If there is no direct divine religious service the dummy variable transit gets 1 and 0 otherwise. Naturally, passengers would not prefer to fly with connecting flights so it is expect to be negatively affecting air travel demand. 4. ECONOMETRIC ESTIMATION data, methodology and results Data availability is main issue when data leadage 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 period. in that location 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 d oes not cover data for all possible on-line O&Ds because some city pairs which are at close place are not meaningful to fly with connecting flights or the concerning flights are not connected severally other. There are 231 city pairs which are served with direct flights, whereas the remaining city pairs are found to be flown by connecting flights via an appropriate domestic hub.Under the assumption of approximately the same number of O&Ds for from each one year, data size entrust be duplicated for the two years period. Airport statistics for all scheduled carriers are utilise in the experimental model as a source of the enumerateent variable. Transfer traffic is removed from the statistics for each city pair, since the proposed model is to estimate sharp O&D passenger by using data specific to the corresponding city pairs. Average prices for each city pair are estimated by using airlines web range. Road distance between the cities is take upn from the web site of t he General 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 grasp rate of the cities are obtained from the Bureau of Statistics in Turkey. Weighted average of the corresponding citys macrocosm is used, while GDP per capita and the labour rate are beingness converted to O&D train. A variety of different models exist for passenger volume estimation. Since no wholeness model guarantees accuracy, airlines in fact comparability forecasts from several different models.Within this set of forecasting methods, the most demand models used are of the simple solemness 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 service-related or market specific input variables, and using cross-sectional normalisation data, th e model is particularly applicable to city-pairs where no air service 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 machine 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 increasing manner. Partial derivation of any separate variable proves aforementioned dealinghip. However, this model can be made sui plank for ternary regressions by app lying logarithmic transformation. Logarithmic form of the gravity model takes the form LogD=? 0 + ? 1LogA + ? 2LogB + ? 3LogC + where ? 0=Log? It is obvious that interdependence is resolved 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 mugwump variables. The results show that some of the variables are interrelate. 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 actually reduce the model fit. As the goal is to obtain a real estimation of the passenger volume, all interrelated variables were included (Grosche et al. 007). Furthermore, it has been said that if the sole subroutine of regression analy sis is prediction or forecasting, then multicollinearity is not a serious problem because the gameer R2, the better prediction(R. C. Geary, 1963). In order to verify stepwise regression fit of the model, stepwise process by backward direction and minimum AICc selection is used. When all main(a) 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 comparison table shows that the studied model efficiency is comparatively 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 faultfinding 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 tail signif icance 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 implies 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 impact on passenger demand estimation. Air transportation and bus transportation seem to be competing each other because of their negative relation. When air service is succeedd by connecting flight which federal agency transit traffic, air transport demand is decreasing. This result is not surprising because tidy sum do prefer to fly directly. some other result is that the number of airlines participating in each O&D market tends to have a positive impact 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 positively related with air transport demand as expected. Table 4 Model Efficiency Benchmark Research Name Level of Forecast Author twelvemonth Independent Variables Observation Adjusted R Square study For Air enrapture In Nigeria combine Adekunle J. Aderamo 2010 Index of AgricultureIndex of ElectricityGDP 23 0. 923 Air Travel Domestic pray Model in Bangladesh integrality Md. Jobair store Alam Dewan Masud KArim 1998 PopulationGDPDistance 31 0. 8 An Econometric summary 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 passenger expect A case study of capital of Egypt Airport Aggregate Dr. Khaled A. Abbas 2003 Population GDPForeign phaeton 88 0. 82 carry for Airravel In USA O&D Dipasis Bhadra 2003 Density, Interaction, Distance, Marketshare, Fare 2424 0. 57 An Aggregate essential 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 analyse 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 unequivocal Percentage Error) value 14. 1 %. demonstrable 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 importantly 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. . deduction This study demonstrated 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 quantitative 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 struc tures, and also useful for the related authority to quantify the benefits of airport capacity expansion and to take into account while airport create plan is being evaluated.It would be advantageous to extend the time period cover by the analysis. This would enable to examine possible differences in elasticity amongst city-pairs. Extending the data back in time would also provide observations of airfares progress. The model efficiency may be improved for even more original 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 cite card statistics of domestic visitors. References S. C. Wirasinghe and A. S. 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