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Traffic signal control is one of the oldest application areas of fuzzy sets in transportation. In general, fuzzy control is found to be superior in complex problems with multi-objective decisions. In traffic signal control, several traffic flows compete for the same time and space, and different priorities are often set to different traffic flows or vehicle groups

The public transport priorities are a very important part of the effective traffic signal control. Normally, the public transport priorities are programmed by using special algorithms, which are tailor-made for each intersection. The experiences have proved that this kind of algorithms can be very effective if some compensation algorithms and the traffic-actuated control mode are used. We believe that using the fuzzified public transport priority algorithms, the measures of effectiveness of traffic signal control can be even better. In this paper, our fuzzy control algorithm of the public transport priorities will be presented.

This paper aims to identify methods of guidance and supervision used in air traffic control training. It also aims to show how these methods facilitate trainee participation in core work activities.

The paper applies the tools of conversation analysis and ethnomethodology to explore the ways in which trainers and trainees act and interact in training situations. The data consist of the video recordings (total 38 hours) and ethnographic material gathered at a vocational institute for aviation and in two aerodrome control tower units.

The trainers used five different instructional strategies with which they guided and controlled the trainees' actions. In simulator training, learning was structured as a process through which the procedural knowledge possessed by the expert controllers was transferred to the trainees through interventions such as orders, test questions and additions. As the trainees progressed to the on‐the‐job training phase, interaction evolved from being trainer‐driven to trainer‐guided. The trainees' performance was fine‐tuned and guided towards local practices of particular work position by means of instructions and information deliveries.

The simulator training and on‐the‐job training appear as two distinctive forms of vocational training with their own aims. In order to improve the quality of the training, it is suggested that greater attention should be given to the ways in which these two separate areas of learning could be better reconciled.

This ethnomethodological study on training interaction complements the understanding of instructional strategies used at different stages of air traffic control training. It is proposed that research into the local and social production of training interaction can shed useful light on the complexities of workplace learning and training interaction, providing a novel perspective for those engaged in practice of vocational education.

The performance of the classical car-following system is easily affected by external disturbances. To enhance the performance of the classical car-following model under sudden external disturbances, a novel car-following model is established to smooth traffic flow.

This paper proposed a Proportion Integration Differentiation (PID) control strategy based on classical control theory and developed a novel car-following model. The linear system theory and Laplace transform are used to derive a closed-loop transfer function. Then, the stability condition is obtained by using the Routh stability criterion and the small gain theorem. Finally, the validity and feasibility of the PID control strategy is proved by numerical simulations.

The analytic results and the numerical simulation results show that both the integration part and the differential part have the positive effect to suppress traffic oscillation efficiently; the collaboration of these two parts has more power to improve the stability of traffic flow. It means that the proposed model integrated with the PID control strategy has the ability of anti-interference and smooth traffic compared with the classical car-following model.

This paper introduces the PID control strategy into the classical car-following system, which enhances the stability of the system and also provides an efficient method for optimizing the traffic flow system.