This study presents a support vector regression algorithm and time series framework to
estimate and predict weekly gasoline consumption in railway transportation industry. For …

Today, more and more, researchers have been trying to apply artificial intelligence (AI) into
the area of transport. Using these methods, they try to solve difficult and complex transport …

Accurate estimation and forecasting of gasoline is vital for policy and decision-making
process in energy sector. This paper presents a hybrid data-driven model based on Artificial …

Compared to the conventional methods, Artificial Neural Networks (ANN) are considered to
be one of the reliable tools for modeling of complex phenomena such as demand. Aim of …

The objective of this work is to develop a machine learning-based Support Vector Machine
(SVM) demand forecasting model and its application in supply chain management. The …

Due to the increasing dependence of human life on energy, it plays a crucial role in the
functioning of the various economic sectors of the countries, potentially and actually. Fuel …

This study presents an adaptive intelligent algorithm for forecasting gasoline demand based
of artificial neural network (ANN), conventional regression and design of experiment (DOE) …

This paper presents an artificial neural network (ANN) approach to gasoline consumption
(GC) forecasting in Lebanon. In order to provide the forecasted gasoline consumption, the …

Real-time bus travel time prediction has been an interesting problem since past decade,
especially in India. Popular methods for travel time prediction include time series analysis …

Gasoline is one of the most sought-after resources in the world, where the need for energy is
indispensable and continuously increasing for human life today. A shortage of gasoline may …