Reliability Analysis of Air-rail Hypernetwork Under the Disturbance of Flight Delays
XU Feng1a,2, Yin Jia’nan2, YANG Wendong2, JIA Meng1b
1. a. School of Management Engineering; b. School of Traffic Engineering, NanJing Institute of Technology, NanJing 211167, China; 2. College of Civil Aviation, NanJing University of Aeronauticsand Astronautics, NanJing 211106, China
Abstract：In order to study the reliability of air-rail network under the disturbance of flight delays. The air-rail weighted hypernetwork model is constructed in this paper, and the disturbance mechanism of flight delays to the air-rail hypernetwork is analyzed. The reliability of China Eastern Airlines’ air-rail hypernetwork under occasional delay scenario and multiple-delay scenario is simulated and analyzed respectively. The results show that: In the case of occasional delay scenario, the reliability of the air-rail hypernetwork is strong, and the failure of a single airport node has only a limited impact on the efficiency of air-rail hypernetwork, and the impact on the network connectivity is minimal. In the case of multiple-delay scenario, the reliability of the China Eastern Airlines air-rail hynetwork is strong under the random disturbance attack mode, but weak under the selective disturbance attack mode. No matter it is under occasional delay scenario or multiple-delay scenario, no matter under the random disturbance attack mode or the selective disturbance attack mode, the reliability of China Eastern Airlines air-rail hypernetwork is superior to its airport network. Measures such as increasing the number of cities, strengthening the protection of hub nodes and strengthening information sharing are conducive to ensuring the reliable operation of air and rail intermodal transport network.
[1] 宁滨,董海荣,郑伟. 高速铁路运行控制与动态调度一体化的现状与展望[J].自动化学报,2019,45(12):2208-2217. NING B, DONG H R, ZHENG W. Integration of train control and online rescheduling for high-speed railways: challenges and future[J]. Acta Automatica Sinica,2019,45(12):2208-2217. [2] 唐秋华,成丽新,张利平.扰动累积下基于机器学习的重调度方式选择[J].中国机械工程,2019, 30(4):472-479. TANG Q H, CHENG L X, ZHANG L P. Rescheduling mode selection accumulation via under recessive disturbance machine learning[J]. China Mechanical Engineering,2019, 30(4):472-479. [3] 郑元,张羽,何蜀燕,等.多源时变延误下考虑信息同步的智能网联车队控制策略[J].交通运输工程与信息学报,2022,22(2):1-18. ZHENG Y, ZHANG Y, HE S Y. Control strategy for connected and automated vehicle platoon considering information synchronization under time-varying delays of multiple sources[J]. Journal of Transportation Engineering and Information,2022,22(2):1-18. [4] 周语,邵荃.基于不确定因素扰动的机场大面积航班恢复规划[J].科学技术与工程,2018,18(16):300-305. ZHOU Y, SHAO Q. Airport large scale flight recovery planning based on uncertainty disturbance[J]. Science Technology and Engineering,2018,18(16):300-305. [5] 王兴隆,石宗北,陈仔燕.空中交通网络模体识别及子图结构韧性评估[J].航空学报,2021,42(7):558-568. WANG X L, SHI Z B, CHEN Z Y. Air traffic network motif recognition and subgraph structure resilience evaluation [J]. Acta Aeronautica et Astronautica Sinica,2021,42(7):558-568. [6] GU Y Y, YANG J H. Early warning model for passenger disturbance due to flight delays[J]. PLoS ONE,2020,15(9): e0239141. [7] ALBERT R, JEONG H, BARABASI A L. Attack and error tolerance of complex networks[J]. Nature,2000, 406:378-382. [8] 王亚浩,钱名军,孙雷.基于复杂网络的我国西部铁路客运网可靠性研究[J].铁道运输与经济,2020,42(3):41-48. WANG Y H, QIAN M J, SUN L. A study on the reliability evaluation of western China railway passenger transport network based on complex network[J]. Railway Transport and Economy,2020,42(3):41-48. [9] 陆秋琴,靳超.煤炭运输公路网络可靠性仿真分析[J].计算机应用,2019, 39(1):292-297. LU Q Q, JIN C. Reliability simulation analysis of coal transportation road network[J]. Journal of Computer Applications,2019, 39(1):292-297. [10] 刘杰,陈锦渠,彭其渊,等.城市轨道交通网络可靠性和运输服务质量评估[J].西南交通大学学报,2020,55(2):1-10. LIU J, CHEN J Q, PENG Q Y, et al. Reliability and service quality evaluation for urban rail transit network[J]. Journal of Southwest Jiaotong University,2020,55(2):1-10. [11] 黄勇,魏猛,万丹,等.西南山地多灾区域道路网络可靠性规律分析[J].同济大学学报(自然科学版),2020, 48(4):526-535. HUANG Y, WEI M, WAN D. Analysis of reliability of road network in mountainous disaster-prone areas in southwest China[J]. Journal of Tongji University (Natural Science),2020, 48(4):526-535. [12] 兑红炎,陈栓栓,段东立,等.面向可靠性的网络级联失效分析[J].运筹与管理,2021,30(11):106-112. DUI H Y, CHEN S S, DUAN D L, et al. Reliability-oriented network cascading failure analysis[J]. Operations Research and Management Science,2021,30(11):106-112. [13] 陈来焕,刘凤霞,孟吉翔.超图的连通度[J].新疆大学学报(自然科学版),2017,34(1):1-6. CHEN L H, LIU F X, MENG J X. The connectivity of hypergraphs[J]. Journal of Xinjiang University (Natural Science Edition),2017,34(1):1-6. [14] ZHAO S, Meng J X. Sufficient conditions for hypergraphs to be maximally edge-connected[J]. Applied Mathematics and Computation,2018,333:362-368. [15] 马秀娟,赵海兴,胡枫.基于超图的超网络相继故障分析[J].物理学报,2016,65(8):088901. MA X J, ZHAO H X, HU F. Cascading failure analysis in hyper-network based on the hypergraph[J]. Acta Physica Sinica,2016,65(8):088901. [16] 张科,赵海兴,冶忠林.超网络的全终端可靠性分析[J].计算机应用研究,2018,37(2):559-563. ZHANG K, ZHAO H X, YE Z L. Analysis for all terminal reliability of hypernetworks[J]. Application Research of Computers,2018,37(2):559-563. [17] 术永昊,郭进利.航空公司超网络的拓扑结构与鲁棒性分析[J].智能计算机与应用,2021,11(12):87-92,96. SHU Y H, GUO J L. Topology and robustness analysis of airline hypernetwork[J]. Intelligent Computer and Applications,2021,11(12):87-92,96. [18] ESTRADA E, RODRÍGUEZ-VELÁZQUEZ J A. Subgraph centrality and clustering in complex hyper-networks[J]. Physica A Statistical Mechanics & Its Applications,2006, 364(1):581-594. [19] DENNING P J. The science of computing: what is computer science[J]. American Scientist.1985, 73(1): 16-19. [20] NAGURNEY A, DONG J. Supernetworks: Decision-Making for the Information Age[M]. Cheltenham: Edward Elgar Publishing,2002. [21] 王志平,王众托.超网络理论及其应用[M].北京:科学出版社,2008. [22] 徐凤,朱金福,陈丹.东航空铁联运双层加权网络的关键节点识别与抗毁性分析[J].铁道运输与经济,2023,45(1):93-100. XU F, ZHU J F, CHEN D. Key nodes identification and invulnerability analysis of China Eastern Airlines air-rail double-layer weighted network[J]. Railway Transport and Economy, 2023,45(1):93-100.
