Design And Implementation Of Urban Traffic Congestion Identification System Based On Grid Model

With the continuous improvement of people's living standards,the city's traffic problems are becoming more and more serious.Therefore,it is very important to quickly and accurately identify the traffic congestion in the city,and it is very important to solve the traffic problems.The research method of lattice model to identify urban traffic congestion.In recent years,the emergence and development of big data technology has brought about endless collection of mass data collection,analysis and application of technical methods,coupled with the development and changes of machine learning technology,the use of big data technology and machine learning technology combined to process and analyze massive floating car trajectory data,It becomes possible to research the urban traffic congestion recognition system based on the grid model.Among the existing methods for studying congestion recognition,most methods need to match the data to the vector map before congestion recognition.This paper uses a method to identify traffic congestion based on the grid model.In this method,it is not necessary to study the algorithm of data and road matching,nor to rely on digital maps with higher accuracy,only to float Car GPS data is processed and analyzed,that is,the data is matched to the constructed grid model for calculation and analysis,so as to complete the identification of traffic congestion.Such a recognition method simplifies the calculation steps and improves the efficiency of traffic congestion recognition.In the design and development of this system,in order to support the storage and use of massive traffic data in the analysis,storage and processing of floating vehicle data,the popular big data technology is adopted;in the data processing and analysis,the Spark distributed computing engine is used,Respectively,real-time calculation and offline calculation of traffic data;in the grid model to construct road network,use DBSCAN clustering algorithm to cluster analysis of traffic data on the grid boundary,thereby constructing the road network;use PCA algorithm to calculate the final Index data to judge traffic congestion.In the overall design of the system,the combined framework of Spring MVC and vue.js is used to achieve the separation of the front end and the back end.The back end is responsible for data analysis and processing.The front end combines Baidu map API and other visualization technologies to obtain the back end analysis and processing.The result data is displayed on the front-end page,thus completing the identification of urban traffic congestion.Then,the system was tested,including functional test and non-functional test.Finally,the paper summarized and prospected.The data currently used in this article is the GPS trajectory data of floating cars in Beijing for one month.Through the technical solutions described above,the traffic congestion situation in Beijing is effectively calculated and displayed on the system page,so Users can quickly understand the traffic congestion in a certain area,and use the traffic congestion as a reference when planning traffic in this area,which is of great help to traffic management.