Short term forecasts of traffic flow at a site may need to be based on inputs from several
other sites and times. Neural networks can be readily used for this purpose, because they …

Artificial neural networks have been considered as an alternative to existing techniques
across a broad range of disciplines including transport. Neural computing has advanced …

This paper explores the application of Time Series Analysis to produce short term forecasts
using automatic traffic counts. Following a brief introduction to Time Series Analysis, model …

This article discusses the relative merits of neural networks and time series methods for
traffic forecasting and summarises the findings from a comparative study of their …

Back-propagation neural networks were trained to make short-term forecasts of traffic flow,
speed and occupancy in the Utrecht/Rotterdam/Hague region of The Netherlands. A …

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 …

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 …

Univariate and multivariate neural network (NN) and autoregressive time series models are
compared with regard to application to the short-term forecasting of freeway speeds …

A hybrid method of short-term traffic forecasting is introduced; the KARIMA method. The
technique uses a Kohonen self-organizing map as an initial classifier; each class has an …

The application of a nonlinear time series model to the prediction of traffic parameters on a
freeway network is investigated. The nonlinear time series approach is a statistical …