This paper attempts to summarise the findings of a large number of research papers
concerning the application of neural networks to transportation. A brief introduction to neural …

In this modern era, traffic congestion has become a major source of severe negative
economic and environmental impact for urban areas worldwide. One of the most efficient …

Car-following (CF) models are fundamental in the replication of traffic flow and thus they
have received considerable attention. This attention needs to be reflected upon at particular …

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 …

Traffic volume is one of the fundamental types of data that have been used for the traffic
control and planning process. Forecasting needs and efforts for various applications will be …

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 …

Transit operations are interrupted frequently by stochastic variations in traffic and ridership
conditions that deteriorate schedule or headway adherence and thus lengthen passenger …

Tracking congestion throughout the network road is a critical component of Intelligent
transportation network management systems. Understanding how the traffic flows and short …

One of the major requirements of advanced traveler information systems (ATISs) is a
mechanism to estimate link travel times. This article examines the use of an artificial neural …

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 …