Paper Info
Title
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 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 to traditional method.
Year
DOI
Venue
2014
10.1109/ITSC.2014.6958080
ITSC
Keywords
Field
DocType
genetic algorithms,intelligent transportation systems,road traffic,road vehicles,alga,glosa,augmented lagrangian genetic algorithm,multivehicle green light optimal speed advisory,speed curve optimization,linear programming,fuel consumption,optimization,speed
Car following,Simulation,Augmented Lagrangian method,Linear programming,Engineering,Intelligent transportation system,Fuel efficiency,Genetic algorithm
Conference
Citations 
PageRank 
References 
4
0.50
13
Authors
3
Name
Order
Citations
PageRank
Jinjian Li151.19
Dridi, M.261.23
El Moudni, A.3345.56