scholarly journals A train dispatching model based on fuzzy passenger demand forecasting during holidays

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Fei Dou Dou ◽  
Jie Xu ◽  
Li Wang ◽  
Limin Jia
Keyword(s):  
Demand Forecasting ◽  
Model Based ◽  
Passenger Demand ◽  
Train Dispatching

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

scholarly journals Electricity demand forecasting in industrial and residential facilities using ensemble machine learning

2020 ◽  
Author(s):  
Rodrigo Porteiro ◽  
Luis Hernández-Callejo ◽  
Sergio Nesmachnow
Keyword(s):  
Machine Learning ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Residential Facilities ◽  
Forecasting Model ◽  
Model Based ◽  
Electricity Demand Forecasting ◽  
Use Of Resources ◽  
Ensemble Machine Learning ◽  
Ensemble Strategy

This article presents electricity demand forecasting models for industrial and residential facilities, developed using ensemble machine learning strategies. Short term electricity demand forecasting is beneficial for both consumers and suppliers, as it allows improving energy efficiency policies and the rational use of resources. Computational intelligence models are developed for day-ahead electricity demand forecasting. An ensemble strategy is applied to build the day-ahead forecasting model based on several one-hour models. Three steps of data preprocessing are carried out, including treating missing values, removing outliers, and standardization. Feature extraction is performed to reduce overfitting, reducing the training time and improving the accuracy. The best model is optimized using grid search strategies on hyperparameter space. Then, an ensemble of 24 instances is generated to build the complete day-ahead forecasting model. Considering the computational complexity of the applied techniques, they are developed and evaluated on the National Supercomputing Center (Cluster-UY), Uruguay. Three different real data sets are used for evaluation: an industrial park in Burgos (Spain), the total electricity demand for Uruguay, and demand from a distribution substation in Montevideo (Uruguay). Standard performance metrics are applied to evaluate the proposed models. The main results indicate that the best day ahead model based on ExtraTreesRegressor has a mean absolute percentage error of 2:55% on industrial data, 5:17% on total consumption data and 9:09% on substation data. 

Passenger Demand Forecasting on Special Bus for Elderly People

1998 ◽  
Vol 33 (0) ◽  
pp. 211-216
Author(s):  
Yasutsugu Nitta ◽  
Gunseop Do ◽  
Yasuo Mori
Keyword(s):  
Elderly People ◽  
Demand Forecasting ◽  
Passenger Demand

scholarly journals Charging for Scarce Rail Capacity in Britain: A Case Study

2008 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Johnson ◽  
Chris Nash
Keyword(s):  
Opportunity Cost ◽  
East Coast ◽  
Demand Forecasting ◽  
Main Line ◽  
Forecasting Model ◽  
Passenger Demand ◽  
Demand Forecasting Model ◽  
The Way

The aim of this paper is to examine the feasibility of identifying an appropriate rail scarcity charge which would make operators pay for their use of rail capacity in line with the opportunity cost of the use of these slots and to give some idea of the likely effects of such charges. The way in which we do this is to use a passenger demand forecasting model, PRAISE, to consider a situation on the East Coast Main Line which is characterized by scarce capacity and a degree of competition.

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