Optimizing urban traffic control using a rational agent

This paper is devoted to developing and evaluating a set of technologies with the objective of designing a methodology for the implementation of sophisticated traffic lights by means of rational agents. These devices would be capable of optimizing the behavior of a junction with multiple traffic signals, reaching a higher level of autonomy without losing reliability, accuracy, or efficiency in the offered services. In particular, each rational agent in a traffic signal will be able to analyze the requirements and constraints of the road, in order to know its level of demand. With such information, the rational agent will adapt its light cycles with the view of accomplishing more fluid traffic patterns and minimizing the pollutant environmental emissions produced by vehicles while they are stopped at a red light, through using a case-based reasoning （CBR） adaptation. This paper also integrates a microscopic simulator developed to run a set of tests in order to compare the presented methodology with traditional traffic control methods. Two study cases are shown to demonstrate the efficiency of the introduced approach, increasing vehicular mobility and reducing harmful activity for the environment. For instance, in the first scenario, taking into account the studied traffic volumes, our approach increases mobility by 23% and reduces emissions by 35%. When the roads are managed by sophisticated traffic lights, a better level of service and considerable environmental benefits are achieved, demonstrating the utility of the presented approach.