基于K-means聚类组合模型的公交线路客流短时预测

Short-term Prediction of Passenger Flow on Bus Routes Based on K-means Clustering Combination Models

预测公交线路短时客流是实现公交动态调度的关键技术.文中通过分析客流特性,构建了基于K-means聚类算法的组合预测模型.首先利用K-means算法将短时客流数据按照时变特征的相似度划分为不同聚类,然后为每类客流数据分别建立最小二乘支持向量机、BP神经网络、自回归滑动平均模型,并考虑天气因素的影响,用遗传算法优化模型参数,对比预测结果,从中选择每个聚类的最佳预测模型构成组合模型.最后以长沙市104路公交客流数据作为实例进行预测分析,结果显示:客流数据时变特征对模型具有选择性,K-means聚类组合模型能够更好地根据不同时段客流数据的时变特征进行分类,因而有利于提高预测绩效;考虑了天气因素的K-means聚类组合模型能进一步提高公交线路的短时预测绩效.

The prediction of the short-term passenger flow on a bus route plays a key role in the daily dynamic bus dispatching system. A combined forecasting model based on K-means clustering algorithm was constructed through analyzing passenger flow characteristics. The historical short-term passenger demand data was divided into different clusters by using K-means algorithm according to the similarity of time-varying demand. Each cluster of the passenger demand was predicted individually by using Least Squares Support Vector Machine (LSSVM), Back Propagation Neural Network (BPNN) and Auto-regressive Moving Average (ARMA). The parameters of LSSVM and BPNN were optimized by the genetic algorithms. The weather effects on passenger flow were also considered. The combination models were formed by a combination of the best prediction models for each cluster. The passenger flow data of Route 104 in Changsha of China was used for the case studies. The results show that the model combinations depend on the differences of the time-varying passenger flow. The K-means clustering method has the ability to classify the time-varying passenger flow data in different periods, which is conducive to improving the prediction performance. The K-means clustering combination models are a promising tool to predict the short-term passenger flow on a bus route, especially when considering the weather effects.