Evaluation of Railway Passenger Comfort with Machine Learning

Abstract

Railway passenger comfort has been considered a growingly important field to attract more passengers from other public transports such as air flights. To allow passengers and train companies to estimate the onboard passenger comfort level, we propose a phone-based hybrid machine learning (ML) model combining pre-train convolutional neural network as a feature extractor and support vector regressor as a predictor. To better demonstrate the capacity of the proposed model, two sub-models of the hybrid model and the same hybrid model but with non-pre-train feature extractor are adopted to be benchmarks. The raw field data is acquired from a corridor between the University of Birmingham station and Birmingham International station using phones, subsequently calculated to corresponding comfort level according to UIC 513. The four models are trained by the dataset in two domains-time domain and frequency domain, then optimized by random search and validated by 10-fold cross-validation. The proposed method yields the best performance with an R2 of 0.988 ± 0.004, a root-mean-square error (RMSE) of 0.028 ± 0.015, and a mean-absolute-error (MAE) of 0.02 ± 0.005. The results of this study underpin the possibility that the railway passenger has the access to quantify the level of comfort and the real-time assistance for the train driver to calibrate the driving style from the proposed system.