| 基于RBF神经网络的高铁客运枢纽客流参数预测方法 |
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| 引用本文： | 谢征宇,贾利民,秦勇,王力.基于RBF神经网络的高铁客运枢纽客流参数预测方法[J].北京理工大学学报,2013,33(S1):44-47. |
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| 作者姓名： | 谢征宇 贾利民 秦勇 王力 |
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| 作者单位： | 北京交通大学 轨道交通控制与安全国家重点实验室, 100044;北京交通大学 交通运输学院, 北京 100044;北京交通大学 轨道交通控制与安全国家重点实验室, 100044;北京交通大学 交通运输学院, 北京 100044;北京交通大学 轨道交通控制与安全国家重点实验室, 100044;北京交通大学 交通运输学院, 北京 100044;北京交通大学 交通运输学院, 北京 100044 |
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| 基金项目： | 国家自然科学基金资助项目(I11A300010) |
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| 摘 要： | 客流参数预测是实现枢纽客流安全状态预警的重要手段,针对枢纽客流参数的预测问题,提出了基于RBF神经网络的高铁客运枢纽客流参数预测方法,通过对高铁综合客运枢纽内瓶颈点的短时客流参数信息进行预测,对客流的拥堵或滞留状态进行及时预警. 实验证明基于RBF神经网络的高铁客运枢纽客流参数预测方法能够对瓶颈点未来短时内的客流参数信息进行较准确地预测,并可较好地反映滞留客流状态.
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| 关 键 词： | 高铁综合客运枢纽 RBF神经网络 客流参数预测 |
| 收稿时间： | 7/8/2013 12:00:00 AM |
Passenger Flow Parameter Prediction Algorithm of Comprehensive Passenger Transport Hub Based on RBF Neural Network |
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| XIE Zheng-yu,JIA Li-min,QIN Yong and WANG Li.Passenger Flow Parameter Prediction Algorithm of Comprehensive Passenger Transport Hub Based on RBF Neural Network[J].Journal of Beijing Institute of Technology(Natural Science Edition),2013,33(S1):44-47. |
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| Authors: | XIE Zheng-yu JIA Li-min QIN Yong and WANG Li |
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| Institution: | State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China |
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| Abstract: | Comprehensive passenger transport hub is a high density passenger flow distribution area. Passenger flow parameters predict method is necessary to forewarn passenger flow congestions and retentions of bottlenecks. In this paper, an algorithm is proposed to predict passenger flow parameter in comprehensive passenger transport hub based on radial basis function neural network. A method based on RBF neural network is studied to achieve short-term prediction of passenger flow parameter in bottleneck. Computational experiments on the actual passenger flow data from a specific bottleneck position in comprehensive passenger transport hub showed that the proposed approach is effective to predict passenger flow parameters of bottleneck position with high forecasting precisions. |
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| Keywords: | comprehensive passenger transport hub RBF neural network passenger flow parameter prediction |
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