Riding Comfort Evaluation Based on Longitudinal Acceleration for Urban Rail Transit—Mathematical Models and Experiments in Beijing Subway

• Main Authors: Huiru Ma, Dewang Chen, Jiateng Yin
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Sustainability
• Subjects: riding comfort; quality of service; urban railway; fuzzy sets; questionnaire survey
• Online Access: https://www.mdpi.com/2071-1050/12/11/4541

Riding comfort is an important index to measure the quality of service for railways, especially for congested urban rail transit systems where the majority of passengers cannot find a seat. Existing studies usually employ the value of longitudinal acceleration as the key indicator to evaluate the riding comfort of vehicles, while there is no validated mathematical models to evaluate the riding comfort of urban rail trains from the perspective of passengers. This paper aims to employ the collected longitudinal acceleration data and passengers’ feedback data in Beijing subway to qualitatively measure and validate the riding comfort of transit trains. First, we develop four regular fuzzy sets based comfort measurement models, where the parameters of the fuzzy sets are determined by experiences of domain experts and the field data. Then a combinational model is given by averaging the four regular fuzzy set models to elaborate a comprehensive measurement for the riding comfort. In order to verify the developed models, we conducted a questionnaire survey in Beijing subway. The surveyed riding comfort data from passengers and the measured acceleration data are used to validate and optimize the proposed models. Two key parameters are deduced to describe all parameters in the fuzzy set models and a meta-heuristic algorithm is applied to optimize the parameters and weight coefficients of the combinational model. Comparing the collected comfort data with the comfort levels and values calculated by different models shows that the averaging model is better than any regular fuzzy set model. Furthermore, the optimized model is better than the averaging model and provides the best accuracy and robustness for riding comfort measurement. The models provided in this paper offer an optional way to measure the riding comfort for further assessment and more comprehensively tuning of train control systems.