Abstract
Many subjects in transportation engineering are often characterized as subjective, ambiguous and vague. In traffic signal control, several traffic flows compete for the same time and space, while each is often assigned its own respective priority. Normally, optimization operates upon several criteria simultaneously, like average delays, maximum queue lengths and percentages of stopped vehicles. So it is likely that fuzzy control is very competitive at complicated real intersections where the use of traditional optimization methods is problematic. In practice, for traffic safety reasons, uniformity is the goal signal control strives to achieve. This goal sets limitations on both the cycle time and the phase arrangements. Hence, traffic signal control in practice is based on tailor-made solutions and adjustments made by the traffic planners. The modern programmable signal controllers with a great number of adjustable parameters are well suited to this process. For good results, both an experienced planner, and fine-tuning in the field, are needed. Fuzzy control has proven to be successful in problems where the process can be controlled by an experienced human operator, but exact mathematical modeling of the problem is difficult or impossible. Thus, traffic signal control is a suitable task for fuzzy control. Indeed, one of the oldest examples of the potential of fuzzy control is a simulation of traffic signal control at an intersection of two one-way streets. Even in this very simple case, the fuzzy control was at least as good as the traditional adaptive control [32].
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Niittymäki, J., Nevala, R., Mäenpää, M. (2002). Fuzzy Logic-Based Traffic Controller. In: Soft Computing in Industrial Electronics. Studies in Fuzziness and Soft Computing, vol 101. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1783-6_7
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DOI: https://doi.org/10.1007/978-3-7908-1783-6_7
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