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 …

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 …

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 …

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 …

This paper presents a new short-term traffic flow prediction system based on an advanced
Time Delay Neural Network (TDNN) model, the structure of which is synthesized using a …

An approach to freeway travel time prediction based on recurrent neural networks is
presented. Travel time prediction requires a modeling approach that is capable of dealing …

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

Estimation of short-term traffic volumes is an important issue in the development of intelligent
transportation systems (ITS). This paper uses genetic algorithms to maximize statistical …

Much of the current activity in the area of intelligent vehicle-highway systems (IVHSs)
focuses on one simple objective: to collect more data. Clearly, improvements in sensor …

The highly non-linear characteristics of the freeway travel time prediction problem require a
modeling approach that is capable of dealing with complex non-linear spatio-temporal …