基于多核最小二乘支持向量机的短期公交客流预测

公交客流是公交规划和运营调度的基础。针对短期公交客流的非线性、随机性和复杂性及支持向量机单核核函数自适应能力较弱的特点，提出一种基于多核最小二乘支持向量机的公交客流预测方法。该方法既考虑到了公交客流的历史数据规律，又顾及到短期公交客流的时变特性，充分利用了相关参数的知识信息。为了保证模型的自适应能力和提高模型的泛化能力，作者提出了综合评价指标，并采用改进遗传算法实现向量机参数优化。最后，结合LS．SVM工具箱，在MATLAB平台上实现长春市短期公交客流的预测。预测结果表明，提出的多核预测方法具有较高的准确性、较强的鲁棒性和自适应能力，在公交客流预测中有具有较好的应用价值。

Prediction of Short-term Pubic Transportation Flow Based on Multiple-kernel Least Square Support Vector Machine

Public transportation flow is the basic data for the public transport planning and operation scheduling. Based on the multiple-kernel least square support vector machine （MLS-SVM）, the paper presented a new pubic transportation flow prediction model according to the non-linear, stochastic and complex characteristics of short-term public traffic flow. The proposed model not only considered the history data, but also took the character of the short-term public flow into account. In order to improve the suitability of the tradition model, a new evaluation index was proposed to portray the training performance of MLS-SVM. Crossover and mutation was modified with the genetic algorithm （GA）, then using the improved CA optimized the penalty parameter and nuclear parameter. The model was applied to Chang-chun city, the result showed that the proposed model had satisfactory perform ache and robustness, and had good potential for predicting the short-term public transportation flow.