Multi-vehicles green light optimal speed advisory based on the augmented lagrangian genetic algorithm

Abstract

The green light optimal speed advisory (GLOSA) is one of the most important applications in the intelligent transportation systems. The existing GLOSA methods can be used to calculate the advisory speed curve, by which the vehicle can arrive at the intersection in the green phase, for the purpose of reducing the trip time and fuel consumption. However, it can not guarantee that the vehicle could arrive at the intersection with the allowed maximum velocity. Therefore, in this paper, the augmented Lagrangian genetic algorithm (ALGA) is proposed for searching the optimized speed curve in all possible speed curves, according to the minimal fuel consumption and the minimal running time. Moreover, the car following model is employed for handling the multi-vehicles problem. The simulation results indicate that, in free-flow conditions, the optimized value can save fuel consumption by 69.3 percent, save total trip time by 12.2 percent comparing it to the traditional method.