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 …

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 …

The ability to timely and accurately forecast the evolution of traffic is very important in traffic
management and control applications. This paper proposes a non-parametric and data …

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent
transportation systems. On the basis of many existing prediction models, each having good …

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 …

In this paper, an aggregation approach is proposed for traffic flow prediction that is based on
the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and …

Traffic stream determining is an essential part of the intelligent transportation management
system. Precise prediction of traffic flow provides a basis for other tasks, like forecasting …

The increase of road traffic in large cities during the last years has produced that long and
short-term traffic flow forecasting is a critical need for the authorities. The availability of good …

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 …

A short-term traffic flow prediction framework is proposed for urban road networks based on
data-driven methods that mainly include two modules. The first module contains a set of …