Adaptive genetic algorithm for on-ramp traffic control

This paper provides a method that calculates the optimal control of on-ramp traffic density from the basis of the dynamic estimate of traffic density. The on-ramp traffic control problem is formulated as follows. The problem is being able to find the optimal inflow of each on-ramp, based on the evaluation of an on-ramp and main line traffic density. We consider the on-ramp traffic density as the decision variable, and treat this control problem as the nonlinear optimization problem that maximizes the evaluation function under restrictions. To maximize the evaluation function under the restrictions, we propose an adaptive Genetic Algorithm. The solution candidates for on-ramp traffic density have inequality restrictions. Therefore, we consider gene values as ratio of the inflow traffic density within the limit of inequality restrictions. Moreover, in the proposed method, genetic operations are not performed in series, but they are performed in parallel and are adaptive. As numerical examples, we adapt statistical data (the on-ramp traffic density) and examine whether the proposed method can control the traffic jam