Using a Neural Network for Forecasting in an Organic Traffic Control Management System

Increasing mobility and rising traffic demands cause serious problems in urban road networks. Approaches to reduce the negative impacts of traffic include an improved control of traffic lights and the introduction of dynamic traffic guidance systems that take current conditions into account. One solution for the former aspect is Organic Traffic Control (OTC) which provides a self-organized and self-adaptive system founding on the principles of Organic Computing. This paper introduces further steps in enhancing the current OTC system with a forecasting technique based on neural networks. The prediction of short-term traffic conditions is an important component of an advanced traffic management system. It enables the system to prevent congestions and is able to react faster to changes in the traffic flow.