Railway passenger flow forecasting can help to develop sensible railway schedules,make full use of railway resources,and meet the travel demand of passengers.The structure of passenger flow in railway networks and the...Railway passenger flow forecasting can help to develop sensible railway schedules,make full use of railway resources,and meet the travel demand of passengers.The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow.Most of the previous studies used only a single feature for prediction and lacked correlations,resulting in suboptimal performance.To address the above-mentioned problem,we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network(F-SAGCN).First,we constructed the passenger flow relations graph(RG)based on the Origin-Destination(OD).Second,the Passenger Flow Fluctuation Similarity(PFFS)algorithm is used to measure the similarity of passenger flow between stations,which helps construct the spatiotemporal similarity graph(SG).Then,we determine the weights of the mutual influence of different stations at different times through an attention mechanism and extract spatiotemporal features through graph convolution on the RG and SG.Finally,we fused the spatiotemporal features and the original temporal features of stations for prediction.The comparison experiments on a railway bureau’s accurate railway passenger flow data show that the proposed F-SAGCN method improved the prediction accuracy and reduced the mean absolute percentage error(MAPE)of 46 stations to 7.93%.展开更多
In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fu...In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.展开更多
Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutiv...Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutivedays (e.g. 120 days).Design/methodology/approach – By analyzing the characteristics of the historical data on daily passengervolume of HSR systems, the date and holiday labels were designed with determined value ranges.In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double LayerParallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of thedaily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result byweighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of dailypassenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume toensure the accuracy of medium-term forecast.Findings – According to the example application, in which the DLP-WNN modelwas used for the medium-termforecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the averageabsolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP)neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalizedregression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for themedium-term forecast of the daily passenger volume of HSR.Originality/value – This study proposed a Double Layer Parallel structure forecast model for medium-termdaily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and WaveletNeural Network. The predict results are important input data for supporting the line planning, scheduling andother decisions in operation and management in HSR systems.展开更多
A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pres...A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation.First,the passenger flow sequence models in the study are broken down using VMD for noise reduction.The objective environment features are then added to the characteristic factors that affect the passenger flow.The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm.It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy,by setting BP,CNN,and LSTM reference experiments.All models’second order prediction effects are superior to their first order effects,showing that the residual network can significantly raise model prediction accuracy.Additionally,it confirms the efficacy of supplementary and objective environmental features.展开更多
In order to improve the competitiveness of smart tourist attractions in the tourism market,this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot.Fir...In order to improve the competitiveness of smart tourist attractions in the tourism market,this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot.Firstly,this paper introduces the big data-driven forecast model of scenic spot passenger flow.Based on the traditional autoregressive integral moving average model and artificial neural network model,it builds a big data analysis and forecast model.Through the analysis of data source,model building,scenic spot passenger flow accuracy,and modeling time comparison,it affirms the advantages of big data analysis in forecasting scenic spot passenger flow.Finally,it puts forward four commercial operation optimization strategies:adjusting the ticket pricing of scenic spots,upgrading the catering and accommodation services in scenic spots,planning and designing play projects,and formulating accurate scenic spot marketing strategies,in order to provide references for the optimization and upgrading of smart tourist attractions in the future.展开更多
地铁站点辐射范围内的兴趣点(point of interest,POI)蕴含着丰富多样的时空特征。在相应时段内,POI会吸引大量交互客流。为精确捕捉POI对地铁客流的影响机理,提出一种多维兴趣点驱动的地铁客流多通道集成预测方法。首先,综合考虑地铁站...地铁站点辐射范围内的兴趣点(point of interest,POI)蕴含着丰富多样的时空特征。在相应时段内,POI会吸引大量交互客流。为精确捕捉POI对地铁客流的影响机理,提出一种多维兴趣点驱动的地铁客流多通道集成预测方法。首先,综合考虑地铁站点属性对客流的吸引作用、各类POI在不同时段内对客流吸引力的动态变化和各地铁站辐射范围内的POI种类数量和距离,分别构建站点客流吸引力公式、POI时间变化权重模型和POI空间流动交互矩阵。然后,提出一种多通道注意力集成的时空神经网络(multi channel attention spatio-temporal neural network,MCASTNN)客流预测模型,采用三分支结构,应用多种注意力机制挖掘多维POI与地铁客流间的深层关联性,通过多通道注意力集成机制进行特征融合,可有效提取地铁客流复杂的站点属性、时间和空间特征。最后,在杭州地铁AFC刷卡数据集上进行验证。结果表明:相较经典机器学习模型和深度学习模型,MCASTNN在居住主导型站点、工作主导型站点和混合商用型站点等多场景和不同时间步长下均具有更高预测精度,相较于Transformer在单步预测任务中均方根误差和平均绝对误差分别平均下降3.09和3.11。对POI特征和MCASTNN进行消融实验,验证了建模方法和各分支特征提取的有效性。该方法可为有关部门优化配置轨道站点资源、合理制定列车开行方案等提供参考。展开更多
文摘Railway passenger flow forecasting can help to develop sensible railway schedules,make full use of railway resources,and meet the travel demand of passengers.The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow.Most of the previous studies used only a single feature for prediction and lacked correlations,resulting in suboptimal performance.To address the above-mentioned problem,we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network(F-SAGCN).First,we constructed the passenger flow relations graph(RG)based on the Origin-Destination(OD).Second,the Passenger Flow Fluctuation Similarity(PFFS)algorithm is used to measure the similarity of passenger flow between stations,which helps construct the spatiotemporal similarity graph(SG).Then,we determine the weights of the mutual influence of different stations at different times through an attention mechanism and extract spatiotemporal features through graph convolution on the RG and SG.Finally,we fused the spatiotemporal features and the original temporal features of stations for prediction.The comparison experiments on a railway bureau’s accurate railway passenger flow data show that the proposed F-SAGCN method improved the prediction accuracy and reduced the mean absolute percentage error(MAPE)of 46 stations to 7.93%.
基金National Natural Science Foundation of China(No.61663021)Science and Technology Support Project of Gansu Province(No.1304GKCA023)Scientific Research Project in University of Gansu Province(No.2017A-025)
文摘In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.
基金supported by the National Natural Science Foundation of China(Grant Nos.72171236 and 71701216)the National Key R&D Program of China(Grant No.2020YFB1600400)+2 种基金the China Scholarship Council(202008360277)the Key Science and Technology Research Program of the Educational Department of Jiangxi Province(Grant No.GJJ200605)the Natural Science Foundation of Hunan Province(Grant No.2020JJ5783).
文摘Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutivedays (e.g. 120 days).Design/methodology/approach – By analyzing the characteristics of the historical data on daily passengervolume of HSR systems, the date and holiday labels were designed with determined value ranges.In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double LayerParallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of thedaily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result byweighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of dailypassenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume toensure the accuracy of medium-term forecast.Findings – According to the example application, in which the DLP-WNN modelwas used for the medium-termforecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the averageabsolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP)neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalizedregression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for themedium-term forecast of the daily passenger volume of HSR.Originality/value – This study proposed a Double Layer Parallel structure forecast model for medium-termdaily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and WaveletNeural Network. The predict results are important input data for supporting the line planning, scheduling andother decisions in operation and management in HSR systems.
基金the Major Projects of the National Social Science Fund in China(21&ZD127).
文摘A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation.First,the passenger flow sequence models in the study are broken down using VMD for noise reduction.The objective environment features are then added to the characteristic factors that affect the passenger flow.The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm.It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy,by setting BP,CNN,and LSTM reference experiments.All models’second order prediction effects are superior to their first order effects,showing that the residual network can significantly raise model prediction accuracy.Additionally,it confirms the efficacy of supplementary and objective environmental features.
文摘In order to improve the competitiveness of smart tourist attractions in the tourism market,this paper selects a scenic spot in Shenyang and uses big data technology to predict the passenger flow of the scenic spot.Firstly,this paper introduces the big data-driven forecast model of scenic spot passenger flow.Based on the traditional autoregressive integral moving average model and artificial neural network model,it builds a big data analysis and forecast model.Through the analysis of data source,model building,scenic spot passenger flow accuracy,and modeling time comparison,it affirms the advantages of big data analysis in forecasting scenic spot passenger flow.Finally,it puts forward four commercial operation optimization strategies:adjusting the ticket pricing of scenic spots,upgrading the catering and accommodation services in scenic spots,planning and designing play projects,and formulating accurate scenic spot marketing strategies,in order to provide references for the optimization and upgrading of smart tourist attractions in the future.
