Research on Public Bicycle Demand Forecasting Based on Historical Data and BP Neural Network

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Meiying Jian ◽  
Jiayu Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Historical Data ◽  
Demand Forecasting

scholarly journals Regional logistics demand forecasting: a BP neural network approach

2021 ◽  
Author(s):  
Lijuan Huang ◽  
Guojie Xie ◽  
Wende Zhao ◽  
Yan Gu ◽  
Yi Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Demand Forecasting ◽  
Indicator System ◽  
Regional Logistics ◽  
Logistics Enterprises ◽  
Logistics Demand ◽  
Bp Neural Network Model

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

Prediction of Coal Production Based on BP Neural Network

2011 ◽  
Vol 217-218 ◽  
pp. 1647-1651
Author(s):  
Ming Ming Wen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Historical Data ◽  
Supply And Demand ◽  
Simulation Software ◽  
Coal Production ◽  
Matlab Simulation ◽  
Coal Supply

It is significance to predict coal production for balancing coal supply and demand in China. The primary goal of this research is the prediction of coal production in china. The method used in the study is known as the BP neural network. The BP neural network is designed with the MATLAB simulation software based on coal production historical data from 1980 to 2007. The studies we have performed showed that the prediction of coal production based on BP neural network is reasonable and valuable. Finally, we get the prediction of coal production from 2010 to 2015, and the prediction indicates that the coal production will increase in the next 5 years.

scholarly journals Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration

2018 ◽  
Vol 9 (1) ◽  
pp. 104 ◽  
Author(s):  
Kejun Long ◽  
Wukai Yao ◽  
Jian Gu ◽  
Wei Wu ◽  
Lee Han
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Bp Neural Network ◽  
Historical Data ◽  
Multiple Source ◽  
Function Parameter ◽  
Support Vector ◽  
Model Parameters ◽  
Svm Model ◽  
Adverse Weather

Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with the Back Propagation (BP) neural network and a common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model, respectively.

Water Demand Forecasting in Hubei Province with BP Neural Network Model

2012 ◽  
Vol 599 ◽  
pp. 701-704
Author(s):  
Zhen Quan Tang ◽  
Gang Liu ◽  
Wen Nian Xu ◽  
Zhen Yao Xia ◽  
Hai Xiao
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Bp Neural Network ◽  
Water Demand ◽  
Demand Forecasting ◽  
Hubei Province ◽  
Demand Prediction ◽  
Water Demand Prediction ◽  
Basic Link ◽  
Bp Neural Network Model

Prediction of water demand is a basic link in water resources plan and management. Reasonable and accurate prediction of storage helps to develop the plan of water resources the next year, which is very favorable to improve the utilization ratio of water resources and reduce the waste of water resources. This paper uses BP neural network to simulate and predict the water content based on the data of water in recent ten years in Hubei province and evaluates the forecast results. The results show that BP neural network for water demand prediction is feasible.

Public Buildings Baseline Load Forecasting Model

2011 ◽  
Vol 403-408 ◽  
pp. 2098-2101
Author(s):  
Qing Ma ◽  
Qi Qiang Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Historical Data ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Adjustment Factor ◽  
Public Buildings ◽  
The Public ◽  
Model Based

Based on the fact that public buildings baseline load is hard to predict effectively,a kind of BP neural networks forecasting model based on FCM optimization preprocesses which combines with adjustment factor is proposed. The method which adopts method of the FCM arithmetic divides the complicated historical data into gather of multiple proxy event day populations. Then, based on BP neural network forecasting model regulated by adjustment factor, public buildings baseline load forecasting model is introduced. The prediction results show that the prediction precision of the model is higher than that of linearity model, and it can predict the public buildings baseline load effectively.

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