The Key-techniques Research Of Congestion Detection System For Traffic Management

The Central Economic Work Conference held at the end of 2018 clarified the positioning of "new infrastructure".As one of the key tasks of "new infrastructure",5G is gradually advancing its research and commercialization process,and the smart city construction has achieved a comprehensive and rapid interconnection mechanism.The 2019 government work reports of many provinces and cities across the country(such as Guangzhou,Shenzhen,Beijing,Henan,Anhui,etc.)clearly deploy the task of developing and improving the construction of smart cities,and will accelerate the construction of smart transportation as the focus of the expected work.Traffic congestion affects the level of urban development to a certain extent,and even seriously hinders the healthy development of the urban economy.Smart transportation can effectively alleviate urban traffic congestion,improve the urban traffic environment,and enhance urban traffic capacity.Urban traffic congestion detection is an important link in the construction of smart transportation.At present,most of the urban traffic congestion detection methods take the road section as the detection unit,which is difficult to analyze the temporal and spatial evolution of congestion,and the overall analysis of urban congestion is not intuitive,and the congestion dredging work is separated from the internal influencing factors of congestion,so it is of great practical significance to carry out the key-techniques research of congestion detection system for traffic management.This paper embodies a multigranular traffic state detection method based on the road section,road point and region,which enables the traffic management work to start from the meso,micro and macro level,and use the multigranular congestion detection results to develop a more scientific congestion guidance scheme.The congestion detection system for traffic management is designed and developed under the drive of intelligent traffic development,which realizes the refined congestion detection from multigranular levels.On this basis,it provides the recommendation service of congestion guidance scheme,which can improve the refined and automatic level of urban traffic management.The main work and innovation of this paper are as follows:1.Discussed the necessity and feasibility of design and development of the congestion detection system for traffic management.This paper analyzes the research status of urban intelligent transportation system,traffic congestion detection,traffic congestion spatiotemporal distribution characteristics and the application of taxi data at home and abroad,and summarizes the existing research problems of congestion detection research.This paper introduces the theory and technical basis of congestion detection system,constructs a traffic management oriented congestion detection system architecture for congestion detection and guidance functions under the framework of urban intelligent traffic management system,designs the system database and functions in detail,and implements the prototype system on this basis.2.Improved the refined road traffic state detection algorithm based on multi-dimensional density clustering.This paper analyzes the shortcomings of density based clustering algorithm in road traffic state estimation.Firstly,GPS points are located based on linear reference system,spatial distance is measured,and speed distance constraints are added to improve the conventional DBSCAN algorithm.Secondly,dynamic segmentation technology is used to divide road sections,road condition event table is used to organize road condition detection results,thus focus on refining road sections at the meso level Condition detection.3.Established the congestion detection model based on CART.This paper analyzes the abnormal rules and patterns of the evolution of spatiotemporal road state,defines four types of congestion points according to the abnormal patterns,and discusses the significance of detecting different types of congestion points.Based on the four types of congestion points,a classification detection model of congestion points is constructed by using CART,which provides decision support for the formulation of congestion guidance schemes at the micro level.4.Improved the regional congestion detection model based on the regional division.This paper explores the shortcomings of the current regional division and the calculation of regional congestion value,uses the binary K-Means clustering algorithm to cluster the OD data of passengers,uses the Voronoi diagram method to determine the regional boundary,so as to improve the traditional regional division method,defines the average operation speed index in the region,which makes support to the congestion guidance scheme at the macro level.5.The ACS-based congestion guidance assistance recommendation model is constructed and used to provide a new traffic management model.This paper studies the multigranular characteristics of congestion events,defines ACS(Apriori Cosine Similarity)to calculate the similarity degree between congestion events,constructs the relationship map model of congestion events,and provides the recommendation results for traffic managers.At the same time,based on the congestion guidance assistance recommendation,a traffic management mode combining active and passive is proposed to improve the efficiency of traffic management at the decision-making level.