摘要
[目的]准确的短时客流预测对于提高超大规模城市轨道交通线网的运营和管理效率具有重要意义,而目前现有研究对于深度挖掘时空关联性仍不够充分,为此基于短时客流的时空规律提出了基于客流时空关联特征的STGCN(时空图卷积神经网络)模型。[方法]首先,通过切比雪夫图卷积网络捕捉超大规模城市轨道交通网络的空间相关性,借助门控循环单元挖掘多时空关联特征下客流的时间相关性;然后,分析待预测车站历史客流数据相关性及OD(起讫点)客流数据相关性,以深入提取时空相关性;最后,结合客流时空关联特征建立STGCN模型。[结果及结论]以上海地铁江苏路站为例,进行短时进站客流预测,结果表明采用时空关联特征参数的预测结果较未加入特征参数的预测精度提高了16%,预测效果较优。
[Objective]Accurate short term passenger flow prediction is of great significance to improve the operation and management efficiency of the ultra-large scale of urban rail transit network.However,the current research on deep exploration of the spatio-temporal correlations is still insufficient.Therefore,according to the spatio-temporal law of short term passenger flow,a STGCN(spatio-temporal graph convolutional neural network)model based on spatio-temporal correlation characteristics of passenger flow is proposed.[Method]Firstly,the spatial correlation of ultra-large scale urban rail transit network is captured by ChebyNet(Chebyshev graph convolutional network),and the temporal correlation of the passenger flow under multi-temporal correlation characteristics is explored with the help of GRU(gated recurrent unit).Secondly,the correlation of the historical passenger flow data of the station to be predicted and that of OD(origin-destination)passenger flow data are analyzed to extract the spatio-temporal correlation deeply.Finally,a STGCN model is established in combination with the spatio-temporal correlation characteristics of the passenger flow.[Result&Conclusion]Taking Jiangsu Road Station of Shanghai Metro Line as an example,a short-term inbound passenger flow prediction is conducted.The result shows that the prediction accuracy with spatio-temporal correlation characteristic parameters is 16%higher than that without the parameters,indicating a better prediction effect.
作者
王润祺
郝妍熙
胡华
方勇
刘志钢
WANG Runqi;HAO Yanxi;HU Hua;FANG Yong;LIU Zhigang(School of Urban Railway Transportation,Shanghai University of Engineering Science,201620,Shanghai,China)
出处
《城市轨道交通研究》
北大核心
2024年第9期91-96,共6页
Urban Mass Transit
基金
上海市科委地方院校能力建设项目(19030501400)
国家自然科学基金项目(52072235)。
关键词
城市轨道交通
短时进站客流预测
时空关联性
时空图卷积神经网络
urban rail transit
short term inbound passenger flow prediction
spatio-temporal correlation
spatio-temporal graph convolutional neural network
