Abstract: Aiming at features of traffic structure in commercial sites, the paper discusses the factors which effect the public traffic modal split. The BP neural network can approach continuous function precisely. So under the strong non-linear mapping and generalization characteristics of BP neural network, a prediction model of commercial sites have been established based on the available commercial sites survey data of Beijing. Meanwhile, this paper has developed the corresponding program taking advantage of the neural network toolbox in MATLAB. When the factors change, it can quantitatively analyze the influence to the proportion of transit trips.

Key words: BP neural network, commercial sites, traffic modal split, proportion of transit trips

摘要： 针对商业网点的交通方式构成特性，首先分析了影响交通方式选择的因素，在北京市商业网点的调查数据基础上，利用人工神经网络强大的非线性映射能力和泛化功能，建立了公交出行比例预测的神经网络模型，并定量的分析了影响因素值的改变对公交比例的影响，预测了在不同策略下商业网点公交出行比例的变化情况。

关键词: BP神经网络, 商业网点, 交通方式, 公交比例