Demystifying the Relationship Between Network Latency and Mobility on High-Speed Rails: Measurement and Prediction

Recent years have seen increasing attention on building High-Speed Railways (HSR) in many countries. Trains running on the railways have a top velocity of up to over 300 km/hour. This makes it become a scenario with unstable connection qualities. In this paper, we propose a novel model that can accurately estimate the mobility status on HSR based on the changing patterns of network latency. Though various impact factors make the prediction complex, we however argue that the recent advance of deep learning applies well in our context, and further we design a neural network model that can estimate the moving velocity based on monitoring network latency’s changing patterns in a short period. In this model, we use a new variable called Round Difference Time (RDT) to describe latency’s changing patterns. We also use the Fourier Transform to extract the hidden time-frequency and use the generated spectrum for estimation. Our data-driven evaluations show that with suitable parameters, this model can get an accuracy of up to 94% on all three lines.