scholarly journals Analysis of Bus Trip Characteristics and Demand Forecasting Based on NARX Neural Network Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Feng Sun ◽  
Wenheng Su ◽  
Weixuan Liu ◽  
Hui Cao ◽  
Dong Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Arima Model ◽  
Demand Forecasting ◽  
The Elderly ◽  
Ic Card ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Narx Neural Network

In recent years, there has been increased interest in the use of bus IC card data to analyze bus transit time characteristics, and the prediction is no longer confined to rail traffic passenger flow prediction and traditional traffic flow prediction. Research on passenger flow forecast for the bus IC card has been increasing year by year. Based on the bus IC card data of Qingdao City, this paper first analyzes the characteristics of one-day passenger flow and passenger flow during subperiods and conducts a separate study on the characteristics of the elderly. The results show that the travel of the elderly is also affected by the weekday and the weekend. Then, based on the ARIMA model and the NARX neural network model, the passenger flow forecasting (10-minute interval) is carried out using the IC card data of No. 1 bus for 5 weekdays. The prediction results show that the NARX neural network model is effective in the short-term prediction of bus passenger flow, and especially, it is more accurate in the peak hour and large-scale data prediction.

scholarly journals Analysis of Bus Trip Characteristic Analysis and Demand Forecasting Based on GA-NARX Neural Network Model

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 8812-8820 ◽  
Author(s):  
Feng Sun ◽  
Xiao-Lin Wang ◽  
Yang Zhang ◽  
Wei-Xuan Liu ◽  
Rong-Ji Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Demand Forecasting ◽  
Characteristic Analysis ◽  
Narx Neural Network

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.

A NARX Neural Network Model To Predict One-Day Ahead Movement of The Stock Market Index of Malaysia

2021 ◽  
Author(s):  
Ali H. Dhafer ◽  
Fauzias Mat Nor ◽  
Wahidah Hashim ◽  
Nuradli Ridzwan Shah ◽  
Khairil Faizal Bin Khairi ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Network Model ◽  
Neural Network Model ◽  
Stock Market Index ◽  
Market Index ◽  
Narx Neural Network

Basketball sports neural network model based on nonlinear classification

2020 ◽  
pp. 1-10
Author(s):  
Rongkai Duan ◽  
Pu Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Linear Part ◽  
Arima Model ◽  
Research Field ◽  
Modeling And Analysis ◽  
Model Based ◽  
Bodily Injury ◽  
Nonlinear Classification

With the continuous innovation of science and technology, the mathematical modeling and analysis of bodily injury in the process of exercise have always been a hot and difficult point in the research field of scholars. Although there are many research results on the nonlinear classification of the basketball sports neural network model, usually only one model is used, which has certain defects. The combination forecasting model based on the ARIMA model and neural network based on LSTM can make up for this defect. In the process of the experiment, the most important is the construction of the combination model and the acquisition of volunteer data in the process of the ball game. In this experiment, the ARIMA model is used as the linear part of the data, and LSTM neural network model is used to get the sequence of body injury. The results of the empirical study show that: it is reasonable to divide the injury of thigh and calf in the process of basketball sports, which is very consistent with the force point of the human body in the process of sports. The results of the two models predicting the average degree of bodily injury for many times are about 0.32 and 0.38 respectively, which are far less than 1. The execution time of the program for simultaneous prediction on the computer is about 1 minute, which is extremely effective.

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