scholarly journals Corrigendum to “Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network”

2017 ◽  
Vol 2017 ◽  
pp. 1-1
Author(s):  
Junfang Li ◽  
Minfeng Yao ◽  
Qian Fu
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Ridership ◽  
Urban Rail ◽  
Forecasting Method

scholarly journals Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Junfang Li ◽  
Minfeng Yao ◽  
Qian Fu
Keyword(s):  
Relative Error ◽  
Total Population ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Contribution Rate ◽  
Urban Rail ◽  
Forecasting Method ◽  
Bpnn Model

Direct forecasting method for Urban Rail Transit (URT) ridership at the station level is not able to reflect nonlinear relationship between ridership and its predictors. Also, population is inappropriately expressed in this method since it is not uniformly distributed by area. In this paper, a new variable, population per distance band, is considered and a back propagation neural network (BPNN) model which can reflect nonlinear relationship between ridership and its predictors is proposed to forecast ridership. Key predictors are obtained through partial correlation analysis. The performance of the proposed model is compared with three other benchmark models, which are linear model with population per distance band, BPNN model with total population, and linear model with total population, using four measures of effectiveness (MOEs), maximum relative error (MRE), smallest relative error (SRE), average relative error (ARE), and mean square root of relative error (MSRRE). Also, another model for contribution rate of population per distance band to ridership is formulated based on the BPNN model with nonpopulation variables fixed. Case studies with Japanese data show that BPNN model with population per distance band outperforms other three models and the contribution rate of population within special distance band to ridership calculated through the contribution rate model is 70%~92.9% close to actual statistical value. The result confirms the effectiveness of models proposed in this paper.

scholarly journals Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation

2019 ◽  
Vol 15 (10) ◽  
pp. 155014771988313 ◽  
Author(s):  
Chi Hua ◽  
Erxi Zhu ◽  
Liang Kuang ◽  
Dechang Pi
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Photovoltaic Power ◽  
Forecasting Method ◽  
Long Short Term Memory

Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid.

Short-Term Traffic Flow Forecasting of Road Based on Spline Weight Function Neural Networks

2014 ◽  
Vol 513-517 ◽  
pp. 695-698
Author(s):  
Dai Yuan Zhang ◽  
Jian Hui Zhan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Weight Function ◽  
Traffic Flow ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Forecasting Method

Traditional short-term traffic flow forecasting of road usually based on back propagation neural network, which has a low prediction accuracy and convergence speed. This paper introduces a spline weight function neural networks which has a feature that the weight function can well reflect sample information after training, thus propose a short-term traffic flow forecasting method base on the spline weight function neural network, specify the network learning algorithm, and make a comparative tests bases on the actual data. The result proves that in short-term traffic flow forecasting, the spline weight function neural network is more effective than traditional methods.

scholarly journals Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hongtai Yang ◽  
Chaojing Li ◽  
Xuan Li ◽  
Jinghai Huo ◽  
Yi Wen ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Model Selection ◽  
Linear Regression ◽  
Regression Models ◽  
Negative Binomial ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Ridership ◽  
Urban Rail ◽  
Selection Of

Direct ridership models can predict station-level urban rail transit ridership. Previous research indicates that the direct modeling of urban rail transit ridership uses different coverage overlapping area processing methods (such as naive method or Thiessen polygons), area analysis units (such as census block group and census tract), and various regression models (such as linear regression and negative binomial regression). However, the selection of these methods and models seems arbitrary. The objective of this research is to suggest methods of station-level urban rail transit ridership model selection and evaluate the impact of this selection on ridership model results and prediction accuracy. Urban rail transit ridership data in 2010 were collected from five cities: New York, San Francisco, Chicago, Philadelphia, and Boston. Using the built environment characteristics as the independent variables and station-level ridership as the dependent variable, an analysis was conducted to examine the differences in the model performance in ridership prediction. Our results show that a large overlap of circular coverage areas will greatly affect the accuracy of models. The equal division method increases model accuracy significantly. Most models show that the generalized additive models have lower mean absolute percentage errors (MAPE) and higher adjusted R 2 values. By comparison, the Akaike information criterion (AIC) values of the negative binomial models are lower. The influence of different basic spatial analysis unit on the model results is marginal. Therefore, the selection of basic area unit can use existing data. In terms of model selection, advanced models seem to perform better than the linear regression models.

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