The short-term traffic flow prediction can help to reduce flight delays and optimize resource allocation. Using chaos dynamics theory to analyze the chaotic characteristics of en-route traffic flow is the basis of short-term en-route traffic flow prediction and ensuring the orderly and smooth state of the en-route. This paper takes the time series of en-route traffic flow extracted from Automatic-Dependent Surveillance Broadcast (ADS-B) measured data as the research object, uses the improved C–C method to reconstruct the phase space, and uses the improved small data volume method to calculate the Lyapunov index to identify the chaos phenomenon of en-route traffic flow. In order to avoid the interference of chaos phenomenon on traffic prediction, the Wavelet Neural Network (WNN) model is established to predict the traffic flow at en-route points. The experimental shows that when the number of iterations is 10,000, the average accuracy of WNN prediction is 0.87173, and the average running time is 6.9335334$$s. According to the experimental results, it can be seen that the smaller number of iterations has more advantages in running time, which greatly reduces the overall running time. At the same time, it indicates that appropriately increasing or reducing the number of iterations in this experiment has little effect on the results.

短期交通流量预测有助于减少航班延误并优化资源分配。利用混沌动力学理论分析航路交通流的混沌特性，是短期航路交通流预测、保证航路有序、畅通的基础。本文以自动相关监视广播（ADS-B）实测数据提取的航路交通流时间序列为研究对象，采用改进的C-C方法重构相空间，并利用改进的小数据体积法计算李亚普诺夫指数来识别途中交通流的混沌现象。为了避免混沌现象对交通预测的干扰，建立小波神经网络(WNN)模型来预测沿途点的交通流量。实验表明，当迭代次数为10000次时，WNN预测的平均精度为0.87173，平均运行时间为6.9335334s。根据实验结果可以看出，迭代次数越少，在运行时间上越有优势，从而大大降低了整体运行时间。同时说明本次实验中适当增加或减少迭代次数对结果影响不大。