This article describes the development and implementation of adaptive transit signal priority (TSP) on an actuated dual-ring traffic signal control system. After providing an overview of architecture design of the adaptive TSP system, the article presents an adaptive TSP optimization model that optimizes green splits for three consecutive cycles to minimize the weighted sum of transit vehicle delay and other traffic delay, considering the safety and other operational constraints under the dualring structure of signal control. The model is illustrated using a numerical example under medium and heavily congested situations. The findings from a field operational test are also reported to validate and demonstrate the developed TSP system. At a congested intersection, it is found that the average bus delay and average traffic delay along the bus movement direction were reduced by approximately 43% and 16%, respectively. Moreover, the average delay of cross-street traffic was increased by about 12%.

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This paper investigates the stability analysis and stabilization of T-S fuzzy systems with time-varying delays. First, a new augmented Lyapunov-Krasovskii functional is constructed, delay-dependent stability criteria in terms of linear matrix inequalities (LMIs) are obtained by combining them with the integral inequality technique and the reciprocally convex combination inequality. Based on the state space decomposition method, some piecewise membership functions are employed to approximate the membership functions. The piecewise membership functions can be locally represented in terms of the convex combinations of the supremum and infimum of some local basis functions. The boundary information of the membership functions is adequately taken into consideration in stability analysis, and then some relaxed membership-function-dependent stability results are obtained. Second, state feedback controllers for fuzzy systems with time-varying delays are presented under the imperfect premise-matching technique, whose membership functions and the number of fuzzy rules are allowed to be designed freely, consequently, the flexibility of controller design is improved. Finally, four numerical examples are given to demonstrate the effectiveness of the presented approaches.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.