基于实时数据特征和XGBoost算法的城市公共交通枢纽客流量预测

doi:10.13229/j.cnki.jdxbgxb.20230956

摘要/Abstract

摘要：

针对城市公共交通枢纽客流量具有随机性、相关性等特点，提出了基于实时数据特征和XGBoost算法的城市公共交通枢纽客流量预测。首先，通过AdamOptimizer算法优化自动编码器，并将城市公共交通枢纽实时客流量数据特征矩阵输入优化后自动编码器，提取数据特征；其次，构建XGBoost模型作为城市公共交通枢纽客流量预测模型，利用差分进化算法迭代寻优模型参数；最后，将数据特征输入至训练后XGBoost模型中，实现城市公共交通枢纽客流量预测。实验结果表明：本文方法RMSE和MAPE更低，预测所用时间更少。

关键词: 实时数据特征, 客流量预测, 自动编码器, XGBoost算法, 差分进化算法

Abstract:

Aiming at the randomness and correlation of passenger flow in urban public transportation hubs， a real-time data feature and XGBoost algorithm based passenger flow prediction for urban public transportation hubs is proposed. Firstly， the automatic encoder is optimized using the AdamOptimizer algorithm， and the real-time passenger flow data feature matrix of urban public transportation hubs is inputted into the optimized automatic encoder to extract data features， Then， an XGBoost model is constructed as the passenger flow prediction model for urban public transportation hubs. Differential evolution algorithm is used to iteratively optimize the model parameters， and the data features are input into the trained XGBoost model to achieve passenger flow prediction for urban public transportation hubs. The experimental results show that the proposed methods have lower RMSE and MAPE， and require less prediction time.

Key words: real time data features, passenger flow forecast, automatic encoder, xgboost algorithm, differential evolution algorithm

中图分类号:

U293.13

姚明辉,王威超,吴启亮,牛燕. 基于实时数据特征和XGBoost算法的城市公共交通枢纽客流量预测[J]. 吉林大学学报(工学版), 2024, 54(11): 3302-3308.

Ming-hui YAO,Wei-chao WANG,Qi-liang WU,Yan NIU. Passenger flow prediction of urban public transportation hubs based on real-time data features and XGBoost algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(11): 3302-3308.

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方法	预测时间/s
本文	0.18
文献［4］	2.05
文献［5］	1.29

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