An Aviation Data Clustering Algorithm Combining the Quantum Collaborative Lemming Algorithm with <italic style="font-style: italic">K</italic>-modes

JIA Shen; HUANG He; GAO Tao; WANG Huifeng

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An Aviation Data Clustering Algorithm Combining the Quantum Collaborative Lemming Algorithm with K-modes

更新时间：2026-04-14

- An Aviation Data Clustering Algorithm Combining the Quantum Collaborative Lemming Algorithm with K-modes
- JOURNAL OF XI’AN JIAOTONG UNIVERSITY (2026)
- 作者机构：
  
  1.长安大学，电子与控制工程学院，710064，西安
  2.长安大学，智慧高速公路信息融合与控制重点实验室，710064，西安
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP301.6
- Received：16 January 2026，
  
  Revised：2026-04-13，
  
  Accepted：14 April 2026，
- 稿件说明：
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JIA Shen, HUANG He, GAO Tao, et al. An Aviation Data Clustering Algorithm Combining the Quantum Collaborative Lemming Algorithm with K-modes[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026.
DOI：

JIA Shen, HUANG He, GAO Tao, et al. An Aviation Data Clustering Algorithm Combining the Quantum Collaborative Lemming Algorithm with K-modes[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026. DOI：

摘要

低空经济的快速崛起催生了城市空中交通、无人机物流等新兴业态，航班运行状态的精准感知与延误风险的智能预判已成为民航安全运营的核心需求。针对低空经济背景下航空运行数据离散属性多、异构性强，以及

-modes聚类存在初始中心敏感、易陷入局部最优和复杂数据适应能力不足等问题，提出一种量子协同人工旅鼠算法优化的

-modes聚类算法。构建QCALA-

modes框架，将量子隧穿初始化引入聚类中心生成过程，并设计自适应步长扰动、多洞穴协同挖掘、量子混沌螺旋觅食和三模式切换逃逸4种策略，在能量因子调控下实现全局搜索与局部开发协同优化。框架中，每个旅鼠个体被编码为一组候选聚类中心，以簇内离散度倒数为适应度函数，通过迭代寻优驱动

-modes完成中心选取与样本划分的联合优化；能量因子随迭代进程自适应衰减，动态切换勘探与开发阶段，在收敛速度与解质量之间实现有效平衡。采用8个标准测试函数、6个公开数据集及和鲸社区539 383条航班数据进行验证。在公开数据集上，QCALA-

modes在Aggregation数据集上的归一化互信息（Normalized Mutual Information，NMI）为0.906 2，优于

means的0.839 5和层次聚类的0.901 0；在Bupa数据集上的NMI为0.138 4，高于

means的0.000 9和层次聚类的0.000 1；在Records数据集上的NMI为0.531 9。4类航班模式延误率簇间极差达到35.44%，有效区分了高低风险运营模式，验证了所提方法的聚类辨识力与业务可解释性；所识别的高延误干线、低风险长途、高风险短途枢纽及午后非枢纽四类模式与实际运营规律高度契合，可直接支撑航空公司差异化调度策略制定、机场容量动态分配及旅客延误预警服务，实现延误风险的精细化主动管控，可为低空经济场景中航空运行状态精细化识别与延误风险分级管控提供方法支撑。

Abstract

The rapid rise of the low-altitude economy has fostered emerging sectors such as urban air mobility and drone-based logistics. Accurate perception of flight operational status and intelligent prediction of delay risks have become core requirements for the safe and efficient operation of civil aviation. To address the challenges posed by aviation operational data in the context of the low-altitude economy—characterized by abundant discrete attributes and strong heterogeneity—as well as the inherent limitations of the

-modes clustering algorithm

including sensitivity to initial cluster centers

susceptibility to local optima

and insufficient adaptability to complex data distributions

this paper proposes a

-modes clustering method optimized by the Quantum Collaborative Artificial L

emming Algorithm (QCALA). A QCALA-

modes integration framework is constructed by introducing quantum tunneling initialization into the cluster center generation process and designing four behavioral optimization strategies: adaptive step-size perturbation

multi-burrow collaborative mining

quantum chaotic spiral foraging

and three-mode switching escape

thereby achieving dynamic co-optimization of global exploration and local exploitation under the regulation of an energy factor. Within the framework

each lemming individual is encoded as a set of candidate cluster centers

with the reciprocal of intra-cluster dispersion serving as the fitness function; iterative optimization drives

-modes to jointly optimize center selection and sample assignment. The energy factor decays adaptively with the progression of iterations

dynamically switching between exploration and exploitation phases to strike an effective balance between convergence speed and solution quality. The proposed method is validated using eight standard benchmark functions

six public datasets

and 539

383 flight records collected from the Heywhale Community. On the public datasets

QCALA-

modes achieves an NMI of 0.9062 on the Aggregation dataset

outperforming

means (0.8395) and hierarchical clustering (0.9010); on the Bupa dataset

its NMI reaches 0.1384

surpassing

means (0.0009) and hierarchical clustering (0.0001); on the Records dataset

the NMI is 0.5319. Experiments on aviation data demonstrate that the optimal clustering performance is obtained at

= 4

with the algorithm converging after 14 iterations. The delay rates of the four identified flight operation patterns are 49.51%

30.67%

66.11%

and 35.13%

respectively

yielding an inter-cluster range of 35.44%

which effectively differentiates high-risk from low-risk operational modes. These results validate the clustering discriminability and business interpretability of the proposed

method

The identified four modes—high-delay trunk routes

low-risk long-haul routes

high-risk short-haul hubs

and afternoon non-hub operations—are highly consistent with actual operational patterns. These modes can directly support the formulation of differentiated scheduling strategies for airlines

dynamic airport capacity allocation

and passenger delay warning services. This facilitates the precise and proactive management of delay risks. Moreover

it provides methodological support for the refined identification of aviation operation statuses and the tiered control of delay risks within low-altitude economic scenarios.

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An Aviation Data Clustering Algorithm Combining the Quantum Collaborative Lemming Algorithm with K-modes

DOI：

摘要

Abstract

关键词

Keywords

references