An experimental comparison of mathematical heavy-duty city bus models to evaluate passenger ride comfort induced by road roughness

IF 1.8 4区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Mathematical and Computer Modelling of Dynamical Systems Pub Date : 2021-01-02 DOI:10.1080/13873954.2021.1887276

Teron Nguyen, Patrick Swolana, B. Lechner, Wong Y.D.

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引用次数: 5

Abstract

ABSTRACT Mathematical models have been used widely to investigate the vehicle-passenger-infrastructure dynamical interaction; however, the responses of various heavy-duty city bus models to estimate ride comfort induced by road roughness are still unknown. In this study, the comparison of dynamical response of buses used in city transport is investigated based on multi-degrees-of-freedom (DOF) bus models developed in MATLAB/Simulink and correlated against passenger ride comfort criteria. The results showed that 9-DOF full bus model is the best option to estimate passenger ride comfort within an error of 2%, as compared to 5-DOF half and 3-DOF quarter bus models with 7% and 20% errors using one wheel-track, and 24% and 36% errors using two wheel-tracks, respectively. The error was calculated as the difference between simulated results from three bus models and the measured data. These mathematical bus models can be customized for estimating passenger ride comfort and surface roughness of dedicated bus/bus-rapid-transit lanes.

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道路不平度对重型城市客车乘坐舒适性影响的数学模型的实验比较

数学模型已被广泛应用于研究车辆-乘客-基础设施的动态相互作用；然而，各种重型城市公交车模型对道路粗糙度引起的乘坐舒适性的响应仍然未知。在本研究中，基于在MATLAB/Simulink中开发的多自由度公交车模型，并与乘客乘坐舒适性标准相关联，对城市交通中使用的公交车的动力响应进行了比较研究。结果表明，9自由度全巴士模型是估计乘客乘坐舒适性的最佳选择，误差在2%以内，而5自由度半巴士模型和3自由度四分之一巴士模型使用单轮轨道的误差分别为7%和20%，使用两轮轨道的误差则分别为24%和36%。误差计算为三个总线模型的模拟结果与测量数据之间的差异。这些公交数学模型可以定制，用于估计乘客乘坐舒适度和专用公交/快速公交车道的表面粗糙度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mathematical and Computer Modelling of Dynamical Systems 数学-计算机：跨学科应用

CiteScore

3.80

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.