Dynamic pricing, which involves changing ticket rates in real-time based on demand, has been shown in a study by Liu et al. to increase load factors and revenue for airlines. Therefore, a simulation model was employed in the study to examine the effects of dynamic pricing on airline revenue and load factors.
In conclusion, modern statistical models, big data analytics, and machine learning techniques are being applied to increase the precision of load factors and flight-based passenger arrival pattern prediction. These methods aid airports and airlines in streamlining operations, enhancing revenue control, and improving the traveler experience. With this level of precision, airports are able to modify their resources in a way that decreases passenger wait times and enhances the traveler experience.
Researchers have recently looked into utilizing social media and big data analytics to improve predictions of passenger arrival times. At the Incheon International Airport in South Korea, traveler arrivals have been predicted using Twitter data, offering essential insights into traveler behavior. By providing more data, projections may be more accurate, allowing airports and airlines to make better operational decisions. For the management of airline revenue, load factor forecasting is equally crucial. Airlines use complex statistical models and machine learning techniques to predict load factors accurately. In a study by Bhatia et al., they utilized a random forest model to predict load factors for Indian airlines, and they were 91% accurate. Airlines are able to improve their operations thanks to this level of accuracy, which lowers overbooking and increases revenue.
The impact of airline pricing techniques on load factor prediction is intriguing. It has been demonstrated that dynamic pricing, which alters ticket rates in real-time in response to demand, improves load factors and revenue for airlines. In a study by Liu et al., the impact of dynamic pricing on airline revenue and load factors were investigated using a computer model. The study discovered that dynamic pricing reduced empty seats while increasing load factors and income.
In conclusion, anticipating passenger arrivals and load factors requires the use of advanced statistical models, big data analytics, and machine learning approaches. Predictions that are accurate help airports and airlines run more efficiently, shorten wait times for customers, and earn more money. Forecast accuracy can be further improved by including dynamic pricing and social media data, which also offers insightful data on price and passenger trends. ORCID NO: 0000-0002-6213-2549, 0000-0003-1289-2715
Anahtar Kelimeler: Machine Learning, Passenger Arrival Prediction, Load Factor Forecasting
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