| 基于历史数据和时间系数的城市轨道站点客流预测 |
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| 引用本文： | 陈满达,李晓龙. 基于历史数据和时间系数的城市轨道站点客流预测[J]. 佳木斯工学院学报, 2012, 0(2): 201-204 |
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| 作者姓名： | 陈满达 李晓龙 |
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| 作者单位： | 同济大学铁道与城市轨道交通研究院,上海201804 |
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| 基金项目： | 城市轨道交通综合测试与试验平台及关键技术研究（2009BAG11B02） |
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| 摘 要： | 在对城市轨道交通站点客流规律、特点进行分析的基础上,通过对上海市轨道交通系统客流历史数据的深入分析,在引入平假日系数的同时,提出了小时系数的概念,并建立了基于平假日系数和高峰小时系数的城市轨道交通站点客流神经网络预测模型,提高了站点进站客流预测的精度,为城市轨道交通系统客流管理提供了理论基础.
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| 关 键 词： | 城市轨道交通 神经网络预测 站点客流 时间系数 |
Prediction of Urban Rail Station Passenger Flow Volume Based on Historical Data and Time Coefficient |
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| CHEN Man-da,LI Xiao-long. Prediction of Urban Rail Station Passenger Flow Volume Based on Historical Data and Time Coefficient[J]. , 2012, 0(2): 201-204 |
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| Authors: | CHEN Man-da LI Xiao-long |
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| Affiliation: | (Research Institute of Railway and Urban Mass Transit,Tongji University,Shanghai 201804,China) |
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| Abstract: | The characteristics of urban rail transit passenger flow volume was analyzed in-depth through the historical data.While the flat holiday coefficient was introduced,a concept of hour coefficient was proposed.Based on historical data,the flat holiday and hour coefficient,this paper established a neural network prediction model to improve the accuracy of urban rail transit site passenger flow forecast,and provided the theoretical basis for passenger flow management of urban rail transit system. |
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| Keywords: | urban mass transit neural network prediction passenger flow volume time coefficient |
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