AGIFORS 59th Symposium

Abstract

Load factor measures a flight’s capacity utilization. For a route with multiple flights, load factor is more relevant and has less fluctuation. In this presentation, we proposed a novel machine learning approach to predict load factor progression at route level based on search and transaction data from Ctrip, China’s leading Online Travel Agency (OTA). Huge volumes of visitor-initiated inquiries are received on OTA’s platform, which with its distinctive ability to tag searches to visitors, provide insights on market demand. Features such as route popularity are derived from search data. Historical load factor and real-time sales are available from transaction records. These information are then put together in the model which predicts the load factor at each future date up until departure. We found that without using price as input, our model achieves high accuracy with an average mean absolute error of 2%~6% depending on future dates to departure.