DOAJ Open Access 2020

Flight Passenger Load Factors Prediction Based on RNN Using Multi-Granularity Time Attention

DENG Yujing, WU Zhihao, LIN Youfang

Lihat Sumber DOI

Abstrak

Accurate prediction of Flight Passenger Load Factors(FPLFs) helps in addressing overbooking and overserving of the flight seats.However,traditional time series-based prediction methods only focus on variation feature of recent daily FPLFs and ignore impacts of other factors,leading to limited prediction performance.To address the problem,this paper proposes a recurrent neural network model using multi-granularity temporal attention mechanism named MTA-RNN.The model constructs a hierarchical attention mechanism to acquire the temporal correlation of FPLFs under different temporal granularities.Also,other factors including the properties of a flight,festivals and holidays are introduced into the model to compute the target FPLFs over a certain period in the future.Experimental results on datasets of real historical FPLFs show that the MTA-RNN model has a higher prediction accuracy than ARIMA,LSTM and Seq2seq models.

Topik & Kata Kunci

Computer engineering. Computer hardware Computer software

Penulis (1)

DENG Yujing, WU Zhihao, LIN Youfang

Format Sitasi

APA MLA BibTeX

Youfang, D.Y.W.Z.L. (2020). Flight Passenger Load Factors Prediction Based on RNN Using Multi-Granularity Time Attention. https://doi.org/10.19678/j.issn.1000-3428.0053569

Akses Cepat

Lihat di Sumber doi.org/10.19678/j.issn.1000-3428.0053569

Informasi Jurnal

Tahun Terbit: 2020
Sumber Database: DOAJ
DOI: 10.19678/j.issn.1000-3428.0053569
Akses: Open Access ✓