Abstract: In order to improve the accuracy of short-term prediction of parking space, based on the data characteristics of free parking space in parking lots, a combined prediction model combined whale optimization algorithm (WOA) and extreme gradient boosting algorithm (XGBoost) was proposed. Firstly, the randomness of the data of free parking space was analyzed, and the singular spectrum analysis (SSA) was used to deconstruct and reconstruct the original data, so as to extract the main components of the original data and eliminate the noise. Secondly, the whale optimization algorithm was used to optimize the main parameters of XGBoost prediction model and find the global optimal parameters. Finally, the accuracy of the proposed prediction model was verified by an example. The test results show that the WOA-XGBoost prediction model that optimizes parameters has higher prediction accuracy and stability.

Key words: traffic and transportation engineering; parking space prediction; singular spectrum analysis; whale optimization algorithm; XGBoost

摘要： 为了提高空闲停车位短期预测的准确性，基于停车场空闲停车位数据特性，提出了一种基于鲸鱼优化算法（WOA）和极限梯度提升算法（XGBoost）的组合预测模型。首先对空闲停车位的随机性进行分析，同时采用奇异谱分析（SSA）对原始数据进行解构和重构，从而对实现原始数据主要成分的提取以及噪声的剔除；其次采用鲸鱼优化算法实现对XGBoost预测模型的主要参数进行寻优，找到全局最优参数；最后通过实例对提出的预测模型的准确性进行了验证。实验结果表明：实现参数优化的XGBoost预测模型具有较高的预测精度以及稳定性。

关键词: 交通运输工程；空闲停车位预测；奇异谱分析；鲸鱼优化算法；XGBoost