Evaluation method of train communication network performance based on normal cloud model and fuzzy analytic hierarchy process

HE De-qiang; LIU Guo-qiang; CHEN Yan-jun; MIAO Jian; YAO Xiao-yang

doi:10.19818/j.cnki.1671-1637.2022.02.025

Volume 22 Issue 2

Apr. 2022

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Article Navigation > Journal of Traffic and Transportation Engineering > 2022 > 22(2): 310-320

HE De-qiang, LIU Guo-qiang, CHEN Yan-jun, MIAO Jian, YAO Xiao-yang. Evaluation method of train communication network performance based on normal cloud model and fuzzy analytic hierarchy process[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 310-320. doi: 10.19818/j.cnki.1671-1637.2022.02.025

Citation:

HE De-qiang, LIU Guo-qiang, CHEN Yan-jun, MIAO Jian, YAO Xiao-yang. Evaluation method of train communication network performance based on normal cloud model and fuzzy analytic hierarchy process[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 310-320. doi: 10.19818/j.cnki.1671-1637.2022.02.025

Citation:

HE De-qiang, LIU Guo-qiang, CHEN Yan-jun, MIAO Jian, YAO Xiao-yang. Evaluation method of train communication network performance based on normal cloud model and fuzzy analytic hierarchy process[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 310-320. doi: 10.19818/j.cnki.1671-1637.2022.02.025

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Evaluation method of train communication network performance based on normal cloud model and fuzzy analytic hierarchy process

doi: 10.19818/j.cnki.1671-1637.2022.02.025

1.
School of Mechanical Engineering, Guangxi University, Nanning 530004, Guangxi, China
2.
CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412001, Hunan, China

Funds:

National Natural Science Foundation of China 52072081

Science and Technology Project of Guangxi AA20302010

Natural Science Foundation of Guangxi 2017GXNSFDA198012

Key Laboratory Project of Manufacturing System and Advanced Manufacturing Technology of Guangxi 19-050-44-S015

More Information

Author Bio:
HE De-qiang(1973-), male, professor, PhD, hdqianglqy@126.com
Received Date: 2021-09-19
Publish Date: 2022-04-25

Abstract

Abstract

To ensure the safety and reliability of high-speed trains, a method for evaluating the performance of train communication networks (TCNs) was studied. A suitable system of performance evaluation indexes was proposed by considering the stringent requirements for TCNs in terms of real-time responsiveness, reliability, and service quality. Fuzzy analytic hierarchy process (FAHP) was used to determine the weights of performance evaluation indexes of TCN. To address the uncertainty of TCN evaluation process, a two-dimensional (2D) evaluation model based on the normal cloud model and fuzzy entropy was constructed. A TCN simulation platform was constructed by using switched Ethernet with large capacity and high reliability, and then used to obtain sample data for each index. The membership degrees of each index were computed by using the 2D evaluation model, and the performance grade of the TCN was determined by the maximum membership degree (from fuzzy theory) principle. Research results show that 60% of the evaluated samples have network performance grades of Ⅰ and Ⅱ when the TCN is in a good state. When the network has high packet loss rate and bit error rate, 40% of the evaluated samples have performance grades of Ⅲ and Ⅳ. Therefore, the result of the 2D evaluation model accurately reflects the state of the TCN. The result is largely consistent with the result from the fuzzy comprehensive evaluation (FCE), indicating that the 2D evaluation model is accurate. However, as it is not possible for the FCE method to exclude the influence of uncertainty in the evaluation process, its result lacks precision. Hence, the proposed method is more suitable for the evaluation of TCN performance. 6 tabs, 15 figs, 32 refs.
- high-speed train,
- communication network,
- normal cloud model,
- network performance evaluation,
- fuzzy analytic hierarchy process

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References(32)

References

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Evaluation method of train communication network performance based on normal cloud model and fuzzy analytic hierarchy process

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