This study reviewed 72 studies in tourism demand forecasting during the period from 2008
to 2017. Forecasting models are reviewed in three categories: econometric, time series and …

The rapid advancement in artificial intelligence and machine learning techniques,
availability of large-scale data, and increased computational capabilities of the machine …

Optimal transmission line rating use is guaranteed with dynamic line rating (DLR). It is a
smart grid technology that foresees variations in meteorological conditions affecting line …

This study aims to combine the modeling skills of deep learning and the domain knowledge
in transportation into prediction of metro passenger flow. We present an end-to-end deep …

Short term electric load forecasting plays a crucial role for utility companies, as it allows for
the efficient operation and management of power grid networks, optimal balancing between …

This paper examines the forecasting performance of ARIMA and artificial neural networks
model with published stock data obtained from New York Stock Exchange. The empirical …

The tourism sector, with its perishable nature of products, requires precise estimation of
demand. To this effect, we propose a deep learning methodology, namely Bayesian …

Short-term passenger flow forecasting is a vital component of transportation systems. The
forecasting results can be applied to support transportation system management such as …

This paper aims to provide, on one hand, an introduction to the theoretical principles of
artificial neural networks (ANN) and on the other, a step-by-step methodology for designing …

In this paper, railway passenger flows are analyzed and a suitable modeling method
proposed. Based on historical data composed from monthly passenger counts realized on …