The growth of vehicles' fleet circulating on urban streets constitutes a very strong tendency in recent years. The main consequence of this phenomenon refers to the increase of urban congestions, of average delays caused by vehicles waiting on traffic lights and of number of stops. Finding strategies to achieve efficient active traffic control in urban centers is a challenge for engineers and analysts. Recently, important research on dynamic networks and Intelligent Transportation Systems using computational intelligence modeling techniques has been done. This paper proposes a new scheme of active control, using optimization algorithms, to dynamically find traffic signal control plans that optimize traffic conditions in delimited networks and corridors. The proposed system includes a time delay predictive model, used in conjunction with evolutionary approaches like genetic algorithms and differential evolution techniques. Conceptual and applied computational representations necessary for the construction of models are presented. Data collected from a big city in Brazil were fed into the commercial microscopic simulator AIMSUN and were used for the practical experiments. Two main experiments were undertaken and statistically compared in order to decide which method is more efficient in optimizing the active traffic signal timing control for the region under study.
- active traffic control
- evolutionary algorithms
- intelligent transportation systems
- traffic lights programming
ASJC Scopus subject areas