An urban traffic flow model integrating neural networks

Over the past few years, artificial intelligence techniques have played important roles in the design of sophisticated traffic management systems. In this paper, we propose a cooperation based neural networks traffic flow model, which aims at being integrated into a real time adaptive urban traffic control system. The modelling is separated into two steps. Firstly, the traffic flow is modelled on a single signalized link by a local neural network. Secondly, based on communications between local neural networks, the traffic flow is modelled over a wide network of junctions. Based on simulated data, the paper concludes on the potentials of neural networks applied to traffic flow modelling. One minute ahead predictions of the queue lengths and the output flows have been obtained with fairly good accuracy. Nevertheless, it emphasizes the real need to further investigate these techniques on experimental data.