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In this article, we analyze the dynamic price dispersion of the Indian domestic airline industry. We develop the power divergence statistic (PDS) for each route and study the average PDS and average airfare movement. We analyze the effect of Average PDS based on a few selected route characteristic variables and market structure variables. Our resea...
Contexts in source publication
Context 1
... the following part, we discuss price disper- sion analysis in terms of the average PDS based on the route characteristics variables and market structure variables. The estimated regression model's (Model 1) result is given in Table 3. ...
Context 2
... Table 3, the independent variable, 'Aver- age population' has the least (absolute) negative significant impact on the dependent variable 'Average PDS' and we find that potential market size has an reverse effect on the 'Average PDS'. This finding supports Mantin and Koo's (2009) study. ...
Context 3
... average lowest airfare (price) has a positive significant impact on the Average PDS (Table 3). Since the 'price' variable implies the overall airfare structure of the Indian domestic airline market, getting a sig- nificant relationship with average PDS helps us to capture the airfare dispersion. ...
Context 4
... the Indian domestic airline market is operated by both FSCs and LCCs, the weekly airfare revision has a significant impact on the Average PDS. The signs of the significant variables' coeffi- cients of the regression model are the same for the overall Average PDS (Table 3) and the separate weekly Average PDS models (Table 4). In our study, the coefficient sign of the independent variables LCC with respect to each week is totally different compared with Mantin and Koo's (2009) study in the context of US airlines. ...
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Citations
... Additional data were also obtained, and these data are now being put to use in order to validate the comparisons of the final model's performances. (Dutta & Santra, 2017) looked at the domestic airline market's dynamic price dispersion. The average power divergence statistic (PDS) and average airfare movement were computed for each route. ...
... The impact of Average PDS was assessed based on a few selected route characteristics and market structure parameters. The research performed by (Dutta & Santra, 2017) demonstrates that prices increase as the departure date draws near if both full service airlines and low-cost carriers are present in the same market. Revenue management and dynamic pricing are often used in the domestic aviation industry in India. ...
The number of passengers traveling on aeroplanes in India is increasing, and so are price changes. Seasonal and special event fluctuations occur in Indian airfares during different periods of the year. The challenge is to accurately predict flight prices. The research work presented in this paper has explored several machine learning models to predict airfares based on multiple characteristics which enhances the flight price prediction accuracy. This research proposes basic and advanced regression models that have been investigated seeking to accurately predict the price of the airline for a passenger to encourage passengers to make the flight ticket booking at the most optimal cost. The basic and advanced regression models explored in this research are Linear Regression, Decision Tree as a regressor, Random Forest as a regressor, XG Boost regressor, K-neighbours regressor, Bagging Regressor and Extra Trees regressor. The performance of these models was evaluated based on the Mean Average Error (MAE), Root Mean Square Error (RMSE), and adjusted R-Square metrics. After the evaluation of all the models that were implemented, Results analysis was discussed which showed that XG boost model achieved the best performance evaluation and the predicted prices were almost matching the actual price values of the flights.KeywordsBasic Machine Learning modelsAdvanced Machine Learning modelsFlight price predictionRegression
... Compared with the case of China, studies into the pricing behavior of Indian airlines are rare. To the best of our knowledge, Dutta and Santra (2016) is probably the only one that analyzes the dynamic price dispersion of the Indian domestic airline industry based on a small number routes. The authors found that route characteristics affect airfare movement as well as airfare dispersion. ...
As two of the fastest-growing economies, China and India share many similarities. However, the air transport sector of the two countries exhibits substantial differences 30 years after deregulation. Private and low-cost airlines have become dominant players in the Indian airline market while the state-owned airlines still enjoy a dominant status in the Chinese market. The econometric analysis of this research suggests that the presence of a low-cost carrier (LCC) on a route has the effect of reducing the airfare and stimulating the demand for air travel in India, no matter the LCC presence is measured by market shares or dummies. Chinese LCCs have similar competitive effects only when the LCC entry is measured by dummy variables. Airport concentration in India could lead to cost savings and thus lower airfares, but this did not occur in China due largely to the lack of a culture of antitrust compliance. In both countries, airport concentration is positively associated with traffic demand. In India this may imply an increase in consumer welfare as airport concentration increases while in China this could mean that consumers would not be able to explore their welfare benefits to the full potential since concentration is positively associated with airfares. We also find that the absolute value of the price elasticity of the Indian market is much larger than that in the Chinese market, probably due to the high LCC penetration rates in India.
