Short-term traffic flow predictions are an essential part of intelligent transportation systems.
Previous research underlines the difficulty in systematically assessing the predictability of …

This paper presents an approach to optimize the short-term traffic prediction performance on
freeways using multiple artificial neural network topologies under different network and …

An approach is presented for optimizing short-term traffic-prediction performance by using
multiple topologies of dynamic neural networks and various network-related and traffic …

To be able to predict reliably traffic conditions over the short term (15 minutes into the future)
may reduce congestion on a transport system. With the emergence of large datasets comes …

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 …

Predicting future traffic conditions in real‐time is a crucial issue for applications of intelligent
transportation systems devoted to traffic management and traveller information. The …

Proper prediction of traffic flow parameters is an essential component of any proactive traffic
control system and one of the pillars of advanced management of dynamic traffic networks …

Due to the nature of traffic itself, most traffic forecasting models reported in literature aim at
producing short-term predictions, yet their performance degrades when the prediction …

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 …

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 …