| 基于时序特征的城市轨道交通客流预测 |
| |
| 引用本文： | 四兵锋,何九冉,任华玲,杨小宝.基于时序特征的城市轨道交通客流预测[J].北方交通大学学报,2014(3):1-6. |
| |
| 作者姓名： | 四兵锋 何九冉 任华玲 杨小宝 |
| |
| 作者单位： | [1]北京交通大学交通运输学院,北京100044 [2]锦州铁道勘察设计院有限公司,辽宁锦州121000 |
| |
| 基金项目： | 科技部“科技支撑”计划项目资助（2011BAG01B01-1） |
| |
| 摘 要： | 通过分析城市轨道交通客流量的时序特征和RBF神经网络的作用机理,将具有不同时序特征的数据分别用不同的神经网络进行处理,建立了基于客流时序特征的并行加权神经网络模型,并用该模型对北京市城市轨道交通各条线路的客流进行预测.结果表明,各线路客流量预测结果的平均绝对百分误差均在10％以下,小于单个神经网络的预测误差,提高了预测精度.
|
| 关 键 词： | 轨道交通 客流预测 组合预测 神经网络 时序特征 |
Urban railway traffic passenger flow forecast based on the timing characteristics |
| |
| SI Bingfeng,HE Jiuran,REN Hualing,YANG Xiaobao.Urban railway traffic passenger flow forecast based on the timing characteristics[J].Journal of Northern Jiaotong University,2014(3):1-6. |
| |
| Authors: | SI Bingfeng HE Jiuran REN Hualing YANG Xiaobao |
| |
| Institution: | 1.School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; 2.Jinzhou Railway Survey and Design Institute Coporation, Jinzhou Liaoning 121000, China) |
| |
| Abstract: | We propose to use the neural network to process the data with timing characteristics by analyzing urban rail transit passenger flow timing characteristics and the mechanism of RBF neural network.A parallel weighted neural network model based on passenger flow sequence characteristics is developed.We use this model to forecast the Beijing urban rail transit passenger flow and get a better forecast result.Each line＇s traffic prediction error is below 10 ％,which is less than the prediction error of a single neural network so as to improve the prediction accuracy. |
| |
| Keywords: | railway traffic passenger flow forecast combination forecasting neural network timing characteristics |
| 本文献已被 维普 等数据库收录！ |
|
