Search

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Objectives: The signal control with fixed phase duration can not be adjusted adaptively according to the real-time road conditions, the improvement of traffic congestion phenomenon is limited. In order to simulate the phase adaptation of traffic lights according to the real-time road conditions, reasonably configure the phase duration of traffic lights and alleviate the traffic congestion, a closed loop system of traffic lights timing optimization and self feedback adjustment which include vehicles flow video acquisition, flow statistics, timing calculation, timing feedback, phase regulation, flow statistics and timing optimization is constructed. Methods: Firstly, combining virtual simulation and computer vision technology, a road traffic simulation scene built on Webster algorithm is constructed to simulate vehicle starting and stopping, driving and signal lights control based on Unity3D. Secondly, the OpenCV library is used to process the traffic flow video captured by Camera Object in the scene, count the traffic flow, calculate the optimal signal cycle, and determine the traffic phase. Finally, the result of timing calculation is applicable to the simulation scene, and further timing optimization is implemented according to the real-time scene to realize the signal phase adjustment and optimization cycle. Results: Traffic flow generation is built on the number of vehicles arriving at the intersection during peak hours. Vehicles are randomly generated in each direction of intersection every minute, including 15-30 vehicles in congestion direction and 5-15 vehicles in non-congestion direction. The initial setting of intersection signal lights is the traditional fixed timing schema, in which the left turn green light is on for 20 s in the north -south direction, the yellow light is on for 3 s, the straight green light is 30 s, and the red light is on for 56 s. The straight red light is on for 56 s, the left turn green light is on for 20 s in the east-west direction, the yellow light is on for 3 s, and the straight green light is on for 30 s. The average waiting time of intersection vehicles has decreased by 17 s, and the traffic volume has increased by 190 vehicles per hour with real time timing optimization scheme. Conclusions: Simulation experiment of traffic signal timing using Unity3D provides a very effective method for the verification of traffic signal timing algorithm. The closed -loop system of signal lights adaptive timing optimization makes up for the limitations of existing traffic lights timing optimization schemes, and achieves real time timing optimization. Simulation results show that the closed loop feedback of signal lamp adaptive timing optimization can greatly reduce the waiting time of vehicles and effectively alleviate traffic congestion. [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]

The traffic flow fluctuation caused by vehicles following each other is not only easy to cause traffic accidents, but also one of the important reasons for the reduction of road traffic efficiency or traffic congestion. Therefore, in this paper, the transfer function of the control system of the vehicle - following model with stimulus - response is established, and then Laplace transform of the transfer function is applied. It is found that the characteristic equation of the control system is a transcendental equation with driver sensitive parameter dependence. The root distribution state of the characteristic equation is estimated to ensure the stability of the control system by using the zero point theorem, and then the range of the driver sensitivity parameters is obtained. The results of numerical simulation show that the range of driver sensitivity parameters obtained in this paper can not only guarantee the stability of vehicle - following system on the micro level, hut also the stability and rapidity of traffic flow on the macro level, and reduces the traffic flow fluctuation objectively, and improves the traffic efficiency. [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]

Copyright of Studia Poliana is the property of Studia Poliana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traffic control is an effective measure to increase the capacity of junctions to alleviate traffic congestion. A class of these approaches deals with coordination of traffic lights in order to allow vehicles traveling in a given direction to pass an arterial without stopping at junctions. Using multi-agent technology a coordinated traffic signal control algorithm based on cooperative mediation is proposed. The coordination of traffic control is modeled as a distributed constraint optimization problem. The optimal asynchronous partial overlay (OptAPO) which is a cooperative mediation based DCOP protocol is used to obtain the optimal strategy. The simulation results show that a strategy which can minimize the global costs can be obtained from the coordinated traffic signal control algorithm based on cooperative mediation. [ABSTRACT FROM AUTHOR]