Congestion resolution continues to remain a challenge even though various signal control systems have been developed for traffic-intersection control. To address this issue, reinforcement learning (RL)-based approaches that focus on solving the associated data-driven problems have been proposed. However, only a few methods have been developed and applied to dual-ring traffic signal control systems. Therefore, we develop an RL-based traffic signal control model for such a system to efficiently allocate the green interval in different oversaturation states of the conflicting phases. The proposed model employs a deep deterministic policy gradient algorithm to optimize the green value in the continuous action space. Further, we develop an extensible prototype learning framework for application to new intersections without additional transfer learning. The proposed model is validated based on morning peak hours in a simulation environment that reflects the actual intersection phase system and minimum green time constraints. The proposed model achieves an average 20% intersection delay reduction, compared with the fixed control method.
Understanding the role of energy dissipation and charge transfer under exothermic chemical reactions on metal catalyst surfaces is important for elucidating the fundamental phenomena at solid–gas and solid–liquid interfaces. Recently, many surface chemistry studies have been conducted on the solid–liquid interface, so correlating electronic excitation in the liquid-phase with the reaction mechanism plays a crucial role in heterogeneous catalysis. In this review, we introduce the detection principle of electron transfer at the solid–liquid interface by developing cutting-edge technologies with metal–semiconductor Schottky nanodiodes. The kinetics of hot electron excitation are well correlated with the reaction rates, demonstrating that the operando method for understanding nonadiabatic interactions is helpful in studying the reaction mechanism of surface molecular processes. In addition to the detection of hot electrons excited by a catalytic reaction, we highlight recent results on how the transfer of the hot electrons influences surface chemical and photoelectrochemical reactions.
The increasing traffic demand in urban areas frequently causes traffic congestion, which can be managed only through intelligent traffic signal controls. Although many recent studies have focused on reinforcement learning for traffic signal control (RL-TSC), most have focused on improving performance from an intersection perspective, targeting virtual simulation. The performance indexes from intersection perspectives are averaged by the weighted traffic flow; therefore, if the balance of each movement is not considered, the green time may be overly concentrated on the movements of heavy flow rates. Furthermore, as the ultimate purpose of traffic signal control research is to apply these controls to the real-world intersections, it is necessary to consider the real-world constraints. Hence, this study aims to design RL-TSC considering real-world applicability and confirm the appropriate design of the reward function. The limitations of the detector in the real world and the dual-ring traffic signal system are taken into account in the model design to facilitate real-world application. To design the reward for balancing traffic movements, we define the average delay weighted by traffic volume per lane and entropy of delay in the reward function. Model training is performed at the prototype intersection for ensuring scalability to multiple intersections. The model after prototype pre-training is evaluated by applying it to a network with two intersections without additional training. As a result, the reward function considering the equality of traffic movements shows the best performance. The proposed model reduces the average delay by more than 7.4% and 15.0% compared to the existing real-time adaptive signal control at two intersections, respectively.
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