基于数据挖掘技术的交通流预测模型

本文针对交通数据挖掘领域的交通流预测问题进行研究和实现.主要对数据挖掘技术应用于交通流数据的特征选择和交通流预测模型的建立提出算法.在对采样数据进行清洗后，以分类与回归决策树作为基学习器，采用梯度提升决策树进行回归拟合，计算出交通数据的特征重要度.并以此重要度作为自适应特征选择的依据.其次，采用聚类算法对选取后的特征数据进行聚类分析，缩小样本大小的同时，同类数据更加相似.最后，以实时数据匹配相应聚类作为训练数据集，使用经过人工鱼群算法优化参数后的支持向量机进行交通流预测.本文结尾通过实验数据论证本文所提出的算法和模型.

Traffic Flow Forecasting Model Based on Data Mining Technology

In this study, traffic flow forecasting in the field of traffic data mining is studied and implemented. This paper presents an algorithm for feature selection of traffic flow data and establishment of traffic flow prediction model based on data mining technology. After cleaning the sampled data, the classification and regression decision tree are used as base learners, and the gradient lifting decision tree is used for regression fitting to calculate the characteristic importance of traffic data. The importance is used as the basis of adaptive feature selection. Secondly, the clustering algorithm is used to cluster the selected feature data, which reduces the sample size and makes the similar data more similar. Finally, real-time data matching and clustering are used as training data sets, and support vector machine is used to predict traffic flow after parameters optimization by Artificial Fish Swarm Algorithm (AFSA). At the end of this paper, experimental data are presented to demonstrate the proposed algorithm and model.