Automatic Discovery of Railway Train Driving Modes Using Unsupervised Deep Learning

Abstract

Driving modes play vital roles in understanding the stochastic nature of a railway system and can support studies of automatic driving and capacity utilization optimization. Integrated trajectory data containing information such as GPS trajectories and gear changes can be good proxies in the study of driving modes. However, in the absence of labeled data, discovering driving modes is challenging. In this paper, instead of classical models (railway-specified feature extraction and classical clustering), we used five deep unsupervised learning models to overcome this difficulty. In these models, adversarial autoencoders and stacked autoencoders are used as feature extractors, along with generative adversarial network-based and Kullback–Leibler (KL) divergence-based networks as clustering models. An experiment based on real and artificial datasets showed the following: (i) The proposed deep learning models outperform the classical models by 27.64% on average. (ii) Integrated trajectory data can improve the accuracy of unsupervised learning by approximately 13.78%. (iii) The different performance rankings of models based on indices with labeled data and indices without labeled data demonstrate the insufficiency of people’s understanding of the existing modes. This study also analyzes the relationship between the discovered modes and railway carrying capacity.

Short-Term Online Forecasting for Passenger Origin–Destination (OD) Flows of Urban Rail Transit: A Graph–Temporal Fused Deep Learning Method

“…For this reason, we used a combination of clustering method and threshold selection to first find the potential groups to which the stations belong and then excluded the station pairs with small similarity. Thus, for calculating Ts(i, j) in W ts (i, j), using the time series as inputs, we first obtained the similarity relationships of different stations based on the clustering method [42] and obtained clusters of stations, denoted as C. Then, for each c ∈ C, a predefined similarity threshold was set to control the number of similarities. Based on the finite similarity relationships, we built the edge set E ts and used Equations ( 17)- (20) to calculate Ts(i, j) in category c ∈ C.…”

Vehicle Detection In Remote Sensing Images

“…Many recent works are based on deep learning for the application of counting moving vehicles or for traffic scene understanding [20,21]. Zheng et al [22], used five deep unsupervised learning models to learn driving modes. However, the data collected from different sensors mounted in the vehicles.…”