摘要
公交线路客流预测是公交调度优化技术的基础研究内容。通过对公交线路站点时段上下车人数主要相关影响因素的分析,并根据改进的BP学习算法,而建立的基于改进的BP神经网络的公交线路站点时段上下车人数预测模型,经哈尔滨市有关调查数据的训练与检验,证明具有较高的预测精度。
Transit line patronage prediction is the core of transit operation optimization technologies. Through the analysis of main relative factors of transit station＇s temporal getting on/off flow, the transit station＇s temporal getting on/off flow forecasting model based on improved BP neural network is built up. The model is trained and testified with data from Harbin, the result proved that the forecast precision of the model is relatively high.
出处
《交通标准化》
2008年第5期186-189,共4页
Communications Standardization
关键词
公共交通
公交线路客流预测
BP神经网络
public transportation
transit line patronage prediction
BP neural network
