This brief presents an innovative algorithm integrated with particle swarm optimization and
artificial neural networks to develop short-term traffic flow predictors, which are intended to …

This paper proposes a novel neural network (NN) training method that employs the hybrid
exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to …

Over the past two decades, neural networks have been applied to develop short-term traffic
flow predictors. The past traffic flow data, captured by on-road sensors, is used as input …

On-road sensor systems installed on freeways are used to capture traffic flow data for short-
term traffic flow predictors for traffic management, to reduce traffic congestion and improve …

Predictions of fundamental traffic variables in the short-term or near-term future are vital for
any successful dynamic traffic management application. Univariate short-term traffic flow …

This paper discusses a neural network development approach based on an exponential
smoothing method which aims at enhancing previously used neural networks for traffic flow …

Sažetak It is critical to implement accurate short-term traffic forecasting in traffic management
and control applications. This paper proposes a hybrid forecasting method based on neural …

Accurate short-term traffic flow forecasting has become a crucial step in the overall goal of
better road network management. Previous research [H. Kirby, M. Dougherty, S. Watson …

Short-term forecasting of traffic parameters such as flow and occupancy is an essential
element of modern Intelligent Transportation Systems research and practice. Although many …

Traffic congestion is one of the main problems related to transportation in developed as well
as developing countries. Traffic control systems are based on the idea to avoid traffic …