Study On The Traffic Parameter Estimation And Signal Control Of Urban Intersection Based On Machine Vision

Central cities are playing an increasingly important role in the coordinated development of national regions.The management and control of urban traffic has gradually become one of the main issues restricting urban construction and economic development.As the urban population continues to increase and people's requirements for transportation modes and traffic quality continue to increase,optimizing the urban road network structure and traffic conditions has become a hot spot of social concern.At the same time,the expansion of the monitoring scope of urban public areas,the refinement of monitoring requirements,and the continuous improvement of monitoring facilities have provided a reliable guarantee for the construction of intelligent transportation systems and the effective operation of platforms.Advanced urban traffic control system is the core of improving traffic efficiency,and reasonable intersection signal control design is an important means to improve urban road capacity and road network traffic conditions,and it is also an important symbol of urban modernization.Therefore,the research on urban traffic control problems has high application value.In response to the current core needs of urban road traffic control,this article focuses on the study of machine vision-based traffic parameter extraction and its role and application value in urban traffic signal control systems.While providing reliable data sources for urban road traffic control,the intelligent development and efficient operation of intersection traffic control provide research results with reference value.To achieve this goal,the main research content includes the following three points:1.Aiming at the problem of obtaining traffic parameters,a road intersection traffic parameter extraction method based on video analysis is designed.The method includes three aspects:(a)A traffic target detection data set VDD(Vehicle Detection Dataset)is produced to match the detection of vehicle targets Task,made a traffic flow parameter data set VCD(Vehicle Counting Dataset),used to evaluate traffic parameter acquisition methods.(b)Proposed a multitarget tracking algorithm based on template matching.(c)Designed a region coding algorithm for Trajectory processing and calculation of traffic parameters.Through experiments and result analysis based on VDD and VCD,the feasibility and effectiveness of the designed method are verified.2.Aiming at the problem of extracting and describing the movement information of traffic targets,based on the camera calibration model and fully considering the characteristics of the road intersection traffic monitoring scene,two camera calibration algorithms are proposed: An offline calibration algorithm based on a virtual grid.(b)For traffic scenes that require changing monitoring perspectives,an online self-calibration algorithm based on a three-dimensional vehicle model is proposed.Based on the results of camera calibration,according to the requirements of urban traffic signal control system for traffic parameters,accurate traffic parameters based on target motion information are obtained,and the effectiveness of the algorithm is verified for several traffic monitoring scenarios.3.In order to achieve the goal of road intersection traffic signal control based on machine vision,three aspects of work have been completed:(a)A road intersection traffic signal control method based on parameter optimization is proposed,and a basic control scheme is formed.(b)A fuzzy logic-based adaptive signal control algorithm is proposed.Under the premise of the basic control scheme,the green light time adjustment adaptive control process based on traffic flow changes is realized.(c)Based on the basic control scheme and the adaptive signal control process,completed the simulation and analysis of road intersection traffic signal control based on machine vision.The road intersection traffic parameter extraction method based on video analysis proposed in the thesis can be adapted to complex traffic scenes,and can more accurately extract important traffic parameter information.The accuracy of traffic flow and traffic composition information can reach more than 90%.In the proposed camera calibration algorithm in the road intersection scene and the motion information extraction method of traffic targets,the two camera calibration algorithms can be used in different scenes.The distance estimation accuracy(91%)of the image scene can be better.To meet actual traffic needs,it can achieve the goal of accurately describing the movement information of traffic targets in a road intersection scene.The proposed machine vision-based traffic signal control strategy can overcome the interference and influence caused by traffic flow fluctuations on traffic signal control,and significantly reduce the average vehicle delay and the number of parking times.It can achieve close to ideal under the condition of unsaturated traffic flow.The traffic control performance under the conditions and the traffic control performance under the saturated traffic flow is also significantly better than the fixed-period signal control strategy.