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Fuzzy logic is known to be suited for dealing with a complex optimization problem with many objectives, many constraints, unclear input information, and vague decision criteria. Controlling the timing of a traffic signal falls in this category of problem. Fuzzy logic is introduced for controlling the timing of a pedestrian crossing signal. The controller is designed to emulate the decision process of an experienced crossing guard. The performance of this control is tested against two types of conventional demand-actuated control: one that uses the traditional green extension and the other that uses modified extension rules. The criteria for evaluation are delays to the pedestrians and the vehicles, and the percentage of vehicles that are stopped. The fuzzy logic controller finds a compromise between two conflicting objectives: minimization of pedestrian delay and minimization of vehicular delay and stops. The evaluation was performed using a microscopic simulation called HUTSIM developed at the Helsinki University of Technology. The fuzzy logic controller performs equally well as or better than conventional demand-actuated control without requiring many parameter settings. Furthermore, the control rules are simple and a compilation of rational decision processes is expressed in natural language.

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Traffic signal control is one of the oldest applications of fuzzy logic, at least in transportation engineering. The aim of this paper is to present a systematic approach to fuzzy traffic signal control and to derive the linguistic control rules based on expert knowledge. Traffic signal programming is generally divided into two problems: firstly, the choice and sequencing of signal stages to be used, and secondly, optimizing the relative lengths of these stages. The rule bases for both problems are introduced in our paper. The results of tested rule bases and field tests of fuzzy control have been promising. The fuzzy signal control algorithms offer better measures of effectiveness than the traditional vehicle-actuated control.

The main goal of this research was to update the basic saturation flow values of signalized intersections. The secondary goal was to analyze the effects of certain external factors (such as weather, road, and traffic conditions) on saturation flow. The updating is based on extensive field measurements and simulations. Altogether, about 39,000 queues were observed in this study. Field measurements at 30 locations were made according to the method described in the Highway Capacity Manual and simulations were done with the Helsinki University of Technology HUT-SIM simulator, which was calibrated and carefully validated for Finnish road conditions. A summary of calibration parameters is also presented. The new base value for straight-through lanes is 1, 940 vehicles per hour; the previous value was 1, 700 vehicles per hour. In general, the updated saturation flow values of different lane types are 5 to 20 percent larger than the previous base values. The saturation flow models of different lane types are described. The effects of geometric and traffic composition factors, such as percentage of turning vehicles, traffic composition, lane width, and approach grade, were examined and modeled. Effects of weather, road surface, light conditions, and speed level were also analyzed. The drop in saturation flow was about 20 to 30 percent under slippery road and snowy conditions. In rainy conditions, the drop was smaller, about 10 percent. The effect of speed on saturation flow is also described. The most important results of this 2-year project are the saturation flow values for different lane types, knowledge of the effect of external factors (especially during winter), and the large database, which can be used for other purposes. The possibility of using special signal control programs under bad road conditions is discussed. With these kinds of programs, better safety and higher capacity can be achieved.