基于DAG-SVM的居民出行方式选择模型

提高居民出行方式的预测精度对于评价交通规划方案、交通策略的效果具有重要意义.应用心理学、行为科学的方法分析了出行决策的思维过程,将出行决策过程结构化,建立出行情景库,并采用主成份法分析了影响方式选择的主要因素,作为支持向量机模型的输入.利用统计学习理论分析了支持向量机与神经网络在建模原理上的区别,建立了基于有向无环图-支持向量机(DAG-SVM)的方式选择模型,阐述了模型的具体步骤.通过实验对不同核函数的预测效果进行了评价,并采用网格法和遗传算法进行参数寻优.结果表明,核函数选择径向基函数效果较理想,参数寻优方法上遗传算法比网格法效果更好.通过优化后,DAG-SVM模型的整体预测精度达到了82.3%,比神经网络提高了近9%.但对出租车出行的预测准确率略低于其他方式,这主要由于出租车常被作为特殊情况下的备选方式,其出行规律性相对较差. 

A Model of Decision Process of Travel Modes Based on DAG-SVM

Improving the accuracy of prediction of travel modes of residents is of a great importance to evaluate the effect of traffic planning and transport strategy.Based on psychology and behavior science, the decision process of travel modes is analyzed.With a structuralized process of decision, a library of travel scenarios is established.A principal component analysis is used to analyze the main factors which have impacts on the decision process of travel modes.The factors are regarded as the inputs of support vector machine (SVM).The differences between SVM and neural network in principles of modeling are analyzed by a statistical learning theory.Then a directed acyclic graph support vector machine (DAG-SVM) model is developed.The results of prediction from different kernel functions are evaluated, and the parameters are optimized by the grid method and genetic algorithm.The results show that among several kernel functions, the radial basis function is the best for prediction.The genetic algorithm is better than the grid method in parameter optimization.The overall accuracy of prediction from the DAG-SVM model is 82.3%, which is nearly 9% higher than that from the neural network model.However the accuracy of prediction for travel by taxi is slightly lower than other ones.This is mainly due to the fact that travel by taxi is an alternative way for residents in particular circumstances, not as regular as other travel modes.