Airfare price prediction is one of the core facilities of the decision support system in civil aviation, which includes departure time, days of purchase in advance and flight airline. The traditional airfare price prediction system is limited by the nonlinear interrelationship of multiple factors and fails to deal with the impact of different time steps, resulting in low prediction accuracy. To address these challenges, this paper proposes a novel civil airline fare prediction system with a Multi-Attribute Dual-stage Attention (MADA) mechanism integrating different types of data extracted from the same dimension. In this method, the Seq2Seq model is used to add attention mechanisms to both the encoder and the decoder. The encoder attention mechanism extracts multi-attribute data from time series, which are optimized and filtered by the temporal attention mechanism in the decoder to capture the complex time dependence of the ticket price sequence. Extensive experiments with actual civil aviation data sets were performed, and the results suggested that MADA outperforms airfare prediction models based on the Auto-Regressive Integrated Moving Average (ARIMA), random forest, or deep learning models in MSE, RMSE, and MAE indicators. And from the results of a large amount of experimental data, it is proven that the prediction results of the MADA model proposed in this paper on different routes are at least 2.3% better than the other compared models.

In all anti-collision protocols of RFID standards, EPCGlobal Class 1 Generation 2 (C1G2) protocol has been most widely used in RFID systems since it is simply, efficient and safety. Similar to most existing anti-collision protocols, The C1G2 protocol initially aims at tag identification of static scenarios, where all tags keep still during the tag identification process. However, in many real scenarios, tags generally move along a fixed path in the reader coverage area, which implies that tags stay the coverage area only for a limited time (sojourn time). The scenarios are usually called mobile RFID systems. Because the multiple tag identification based on a shared wireless channel is random, tags entering the reader coverage area earlier may be identified later (random later identification phenomenon). The phenomenon and the limited sojourn time may let some tags lost. In this paper, we propose an improved C1G2 protocol with first come first served feature in mobile RFID systems. The protocol can overcome the RLI phenomenon effectively and retains good initial qualities of C1G2 protocol by modifying it slightly. Simulation results show that the proposed protocol can significantly reduce the numbers of lost tags in mobile RFID systems. The idea of the paper is beneficial for redesigning other existing tag anti-collision protocols so as to make these protocols adapt to mobile RFID systems.