Abstract
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.
Highlights
With the development of rail transit and the improvement of people’s living condition, high-speed, on-time and safety are no longer the only goals of modern railway systems [1]
We developed four regular fuzzy set models to compute the instantaneous riding comfort value according to acceleration rate from train control systems and more importantly, the parameters in fuzzy models are determined by domain experience and field data
By using the fuzzy set theory, four comfort measurement models are developed and the parameters of membership functions are determined by the domain experience and the distribution of collected field data in Beijing subway Yizhuang line
Summary
With the development of rail transit and the improvement of people’s living condition, high-speed, on-time and safety are no longer the only goals of modern railway systems [1]. In these situations, train riding comfort is especially significant, since any sudden accelerating or braking may tumble the standing passengers and cause potential safety risks. There are many measurement methods for riding comfort, including Sperling fitted index, Diekeeman index, Janeway comfort factor and so forth [2] These methods consider that the improvement of riding comfort is one key to improve railway passengers’ amenity [3] and the concept of riding comfort is related to two categories of measurement: the measurement of physical quantities that affect riding comfort and the measurement of the corresponding feeling of human beings [4], which is too complex for computing in practice. The vehicle-track interaction [8], overhead system [9], ground-borne vibration [10] and the environment [11] may affect the feeling of passengers, and there is no universal standard measurement method for riding comfort till
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