Traffic flow modeling and control using artificial neural networks

In this article we use an artificial neural network technique to model and control highway traffic in a single lane with no on- or off-ramps. The developed controllers generate the speed commands for each section of the lane that vehicles need to follow in order to achieve a desired traffic flow density distribution along the lane. In today's traffic, these speed commands could be communicated to drivers, who would then have to respond to them. This raise human factors issues that need further investigation in order to assess the possible benefits. In an automated highway, speed commands cn be communicated to the vehicle's computer control system and followed directly without human errors or delays. We simulate an automated highway environment with such a system to alleviate congestion. We demonstrate that the use of feedback control on the macroscopic level could bring dramatic improvements to traffic flow characteristics.