The aviation industry has experienced significant growth in recent decades, profoundly affecting global travel and economic development. Predicting Air Passenger Demand (APD) is essential for strategic planning in aviation, impacting route choices, fleet allocation, and pricing. This study utilises advanced statistical techniques, specifically Generalized Additive Models (GAM) and Spatial GAM, to analyse the complex determinants of APD, incorporating non-linear relationships and spatial dependencies. The research evaluates the impact of geoeconomic factors, service-related variables, and built-environment features on APD. Key determinants include population density, per capita income, GDP, flight frequency, airfare, travel time, tourist attractions and educational centres. The study uses comprehensive data from authoritative sources such as the Airports Authority of India and NITI Aayog, covering April 2019 to March 2023. Findings reveal that the GAM model explains 87.7% of APD variability, highlighting factors like year, population, airfare, flight duration, train fare, population density, tourist spots, and sports centres. The Spatial GAM model, with an adjusted R-squared value of 0.925 and 94.5% deviance, explained accounts for spatial variations and identified key interaction effects among various factors. These insights inform policy recommendations to enhance APD and ensure balanced urban development, including integrating urban planning with aviation infrastructure, adjusting pricing strategies, promoting tourism and recreational facilities, addressing socio-economic disparities, and enhancing connectivity and service frequency. This study provides a robust framework for better decision-making and strategic planning in aviation, supporting the development of effective policies to meet evolving passenger needs. Future research should explore long-term trends and emerging factors to understand APD dynamics further.
