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The optimization of intersection signal control can improve traffic efficiency, reduce congestion degree, and improve traffic safety. Aiming at implementing the coordinated adaptive traffic signal control (ATSC) across large-scale arterial network, multi-agent reinforcement learning (MARL) has been widely concerned and lucubrated. Nevertheless, the existing MARL-based ATSC studies suffers from several limitations: (1) While most existing researches focused on the mobility performance of controlled corridor, there calls for a methodology that aims at combine multi-objective performance on traffic safety, efficiency, and network coordination simultaneously; (2) Most methods ignore the correlations between multiple agents, nor considers the spatial–temporal dependencies among the corelated neighboring intersections due to high communications requirements, which can hardly be achieved in real adaptive coordination control. To overcome the aforementioned difficulties, a multi-objective reinforcement learning model (NACRL) for network-wide coordinated signal control is proposed. Firstly, to enforce a coordinated network control with safety and efficiency considerations, a reward mechanism inspecting both traffic safety and traffic efficiency indicators was designed to achieve ideal performance in terms of mobility, safety and smooth. Secondly, the proposed NACRL conducted a centralized training-decentralized execution framework, this overcomes the critical limitation of data transmission in the field implementation while explicitly analyzing the traffic state over the entire network instead of examining each isolated intersection. Last but not least, the proposed algorithm utilized the attention mechanism to dynamically capture the sophisticated spatial–temporal dependencies over the complex arterial network, which aids the better coordinated control over multi-agents deployed at the intersections across the corridor. To testify the effeteness of the proposed algorithm, extensive experiments were implemented in both large-scale synthetic traffic grid and real-world arterial network. The experiment demonstrated that the proposed NACRL algorithm outperforms other state-of-the-art baselines with simultaneously improved performance in terms of traffic safety, traffic efficiency and network coordination, as well as improved algorithm convergence and interpretability. [ABSTRACT FROM AUTHOR]

• Proposed a semi-actuated control logic for superstreet intersection. • The model uses a fuzzy-logic type 2 system and derives control parameters. • A VISSIM simulation estimates the quality of the proposed control algorithm. • The model is tested on a field superstreet under various traffic demand patterns. The paper presents a novel approach to RCUT control, based on the interval Type-2 fuzzy system (IT2FS). RCUT design, among other various alternative intersections, proposes dislocation of left and through movements from the main intersection for an increase in safety and efficient traffic conditions. This concept assumes the rise of two new U-turn intersections downstream from the main intersection that can be controlled by traffic lights. RCUT design is justified when U-turn traffic demands, along with demands from and to minor streets, are significantly lower than arterial street ones. The semi-actuated traffic control seems the most appropriate control mode that should be applied at the RCUT. Evaluation of semi-actuated traffic control based on IT2FS is compared with other traffic control modes. The results show that semi-actuated traffic control, based on IT2FS, can generate statistically better results than other well-known controls. [ABSTRACT FROM AUTHOR]

Abstract: It is important to establish the decision of traffic safety planning by forecasting the development tendency of traffic accident according to the related data of traffic safety in former years. In order to solve the drawbacks of BP neural network, a novel approach which combines particle swarm optimization and support vector machine (PSO–SVM) is presented to traffic safety forecasting. Firstly, influencing factors of traffic safety and evaluation indexes are analyzed, then traffic safety forecasting model by PSO–SVM is established according to the influencing factors. Finally, the data about traffic safety in China from 1970 to 2006 are applied to research the forecasting ability of the proposed method. The experimental results show that traffic safety forecasting by PSO–SVM is better than that by BP neural network. [Copyright &y& Elsevier]