An adaptive approach to enhanced traffic signal optimization by using soft-computing techniques

Authors:
Eusebio Angulo;Francisco P. Romero;Ricardo García;Jesús Serrano-Guerrero;José A. Olivas
Affiliations:
Escuela Superior de Informática, Universidad de Castilla-La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain;Escuela Universitaria de Ingeniería Técnica Industrial, Universidad de Castilla-La Mancha, Avda. Carlos III, s/n, 45071 Toledo, Spain;Escuela Superior de Informática, Universidad de Castilla-La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain;Escuela Superior de Informática, Universidad de Castilla-La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain;Escuela Superior de Informática, Universidad de Castilla-La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 5
Cited 2

Delay Analysis for the Fixed-Cycle Traffic-Light Queue

Transportation Science
The Research of Single Intersection Signal Control Method in Intelligence Transportation System

ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
An iterative approach to enhanced traffic signal optimization

Expert Systems with Applications: An International Journal
Urban traffic signal timing optimization based on multi-layer chaos neural networks involving feedback

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Applying a Traffic Lights Evolutionary Optimization Technique to a Real Case: “Las Ramblas” Area in Santa Cruz de Tenerife

IEEE Transactions on Evolutionary Computation

Automatic linguistic report of traffic evolution in roads

Expert Systems with Applications: An International Journal
Genetic optimization of a vehicle fuzzy decision system for intersections

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

Abstract

This paper presents an application of diverse soft-computing techniques to adaptive traffic light controls. The proposed methodology consists of two main phases: off-line and on-line. First, clustering techniques and optimization methods are used at the off-line stage to discover the prototypes which characterize the traffic mobility patterns at an intersection. After this process an optimum timing plan is decided for each mobility pattern detected. In the on-line phase, a prediction model is then constructed on the basis of the prototypes found. Fuzzy Logic based techniques are used to formally represent the prototypes in the prediction model and these prototypes are parametrically defined through frameworks. During the on-line phase an intelligent transportation system, by using the prediction model, matches the current traffic conditions to the mobility patterns detected at the off-line stage in order to identify the most suitable one to be used. The use of these techniques supposes a substantial contribution to the significance of the prediction model, making it robust in the face of anomalous mobility patterns, and efficient from the point of view of real-time computation.