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中文摘要：
公交站点短时客流预测是公交调度决策的基础,文中设计了一种基于AP聚类算法的支持向量机用于公交短时客流预测.该方法利用AP聚类算法将客流调查数据划分为若干个聚类子集,对每一子集建立支持向量机预测模型,并采用遗传算法对预测模型的参数进行优化选择.该方法在兰州市快速公交站点客流数据统计的基础上进行实例分析,结果表明：设计的遗传算法可以有效解决支持向量机模型中的参数优选问题,使用AP聚类算法对客流数据进行分类可以提高支持向量机的预测精度,该预测方法可有效的对公交车站客流进行短时预测.
英文摘要：
Short-term passenger flow forecasting on bus stop is an important technical support for bus dispatch strategy. A Support Vector Machine （SVM） method based on Affinity Propagation （AP） is developed to forecast short-term passenger flow based on the characteristic analysis. The AP clustering algorithm is used to divide the passenger flow into several cluster subsets and the prediction model of SVM is established based on each subset. Then, the parameters of prediction model are optimized by genetic algorithms. This forecasting method is validated on some bus stations on Lanzhou bus rapid transit. The results show that the designed genetic algorithm can effectively solve the problem of pa- rameter optimization in SVM model, the classified passenger flow data using the AP algorithm can improve the forecasting accuracy of SVM and this method is suitable for the short-term passenger flow forecasting.
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