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At present, the problem of airspace resource shortage caused by the surge of air traffic is becoming more and more prominent. In order to alleviate this situation, the study of aircraft rerouting based on the flow management was carried out. Firstly, the airspace environment was discretized by rasterization, and the airspace was divided into different types of raster regions according to the congestion degree of waypoint flow. Secondly, it was used to model by improving the reward function of Markov decision process in reinforcement learning. Based on the ε-greedy strategy, Q-Learning algorithm was used for iterative solution, and the corresponding parameter value was explored and compared to improve the applicability of the results. Finally, the optimal path and the corresponding performance indexes under different parameter assignments were calculated through simulation. The results show that the model and algorithm can be used to search for an appropriate rerouting path for the congested airspace in a certain period of time, so that the aircraft can avoid the congested waypoints, shorten the delay time in the air, and effectively improve the status of airspace congestion. [ABSTRACT FROM AUTHOR]

The new intelligent transportation system plays an important role in improving traffic flow, optimizing fuel efficiency, reducing delays and improving the overall driving experience. Nowadays, traffic congestion is a very serious problem that disturbs human beings, especially the intersection with dense traffic in some cities may be more serious. Improves the reward mechanism of signal control system, the reward mechanism of all intersections to each intersection sharing a unique reward, and through the combination of intensive sampling strategy and multi-intersection signal control, using the popular deep reinforcement learning to solve the traffic signal timing problem. Simulation experiments are based on the current international mainstream traffic simulation software (SUMO). The experimental results show that the improved deep reinforcement learning multi-junction signal control method has better control effect than the traditional reinforcement learning method. [ABSTRACT FROM AUTHOR]

Focusing on the problem with traffic congestion under changing environmental conditions, this paper proposed a trajectory reward light (TR-light) model by combining reinforcement learning, neural network, multi-agent and traffic simulation technology to optimize the traffic at multi-intersections. This method had considerable merits in the following aspects. The traffic organization plan was formulated based on traffic lights; multi-agent reinforcement learning was used on traffic light control; regional traffic organization was optimized through the coordination of traffic lights; the agent implemented trajectory reconstruction after the execution of each behavior so as to change the vehicle travel path without changing the OD pair, and to calculate the final reward of the agent according to the plan and reconstructed trajectory. Finally, it conducted a traffic simulation experiment through SUMO. The comparison of traffic indicators verifies that the proposed model improves the smoothness of the road network and the traffic state at the multi-intersections. Experiments show that the model is feasible and effectively mitigates the traffic congestion. [ABSTRACT FROM AUTHOR]

For intersection traffic congestion phenomenon, combined fog computing and reinforcement learning theory, this paper proposed a traffic light control model FRTL. The model performed intelligent coordinated control of traffic lights based on real-time traffic flow information. The fog node uploaded the collected real-time traffic flow information to the fog server, and the fog server realized information sharing on the fog platform, and the fog platform combined the processed shared data and Q learning to formulate a traffic light control algorithm. The algorithm used the detected real-time traffic data to calculate a suitable traffic light timing scheme, which was finally applied to the traffic light. The simulation results show that compared with the traditional time-phase control method and the main road control method (ATL), the FRTL control method improves the throughput of intersections, reduces the average waiting time of vehicles, and achieves the goal of properly regulating traffic lights and alleviating traffic congestion. [ABSTRACT FROM AUTHOR]