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.

scholarly journals Forecasting Methodology of Maritime Passenger Demand in a Tourist Destination

2012 ◽  
Vol 21 (3) ◽  
pp. 183-190 ◽  
Author(s):  
Davor Krasić ◽  
Petra Gatti
Keyword(s):  
Key Words ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Intention To Use ◽  
Tourist Destination ◽  
Forecasting Models ◽  
Passenger Demand ◽  
Theoretical Assumptions ◽  
Tourist Demand

Maritime passenger demand forecasting is a task that is almost always present in the development studies of passenger ports, both due to operational and investment requirements. If a port belongs to a tourist destination, then there is a reasonable intention to use the forecasting model in order to establish the dependence between the passenger and tourist demand. Since the reliability of forecasting depends to a great extent on the quality and availability of data, the forecasting model is often a compromise between the theoretical assumptions and practical possibilities. This paper presents the approach to maritime passenger demand forecasting using a case study of the tourist destination – Poreč, which has been the strongest destination in Croatia regarding tourist traffic for many years. The presented forecasting models can serve as one of the guidelines for further study of the relations between traffic and tourism. KEY WORDS: forecasting maritime passenger demand, forecasting tourist demand, traffic and tourism

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.

Sign in / Sign up

Export Citation Format

Share Document