Research On Traffic Congestion Governance Based On Traffic Flow Prediction

With the rapid development of China’s national economy and the continuous deepening of urbanization,the number of motor vehicles is increasing,making traffic congestion problems increasingly serious.Transportation is an extremely important part of urban life.The level of urban transportation development reflects the modernization of a city.Without high-quality urban transportation,high-quality urban life and high-efficiency urban economy cannot be mentioned.However,at present,large,medium and small cities in China are facing different levels of traffic congestion.These problems seriously restrict the sustainable development of cities.Therefore,it is very necessary to study and control traffic congestion.It is the current urban development.An important topic.At present,some countermeasures,measures and means for urban road traffic congestion problems cannot be organically unified in the implementation stage of flexibility,principle and delay.Therefore,the effectiveness of traffic congestion cannot be alleviated.Immediately.Based on the construction and development of intelligent transportation system,it can provide an effective way to maximize the information resources of transportation system,balance traffic flow and alleviate traffic congestion.Traffic flow prediction is an important part of intelligent transportation system.Because road traffic is a complex and open system with time-varying nonlinearity,real-time road condition information lacks foresight in supporting traffic management and decision-making,and cannot actively prevent traffic events.Therefore,only by mastering the information of advanced road conditions can we respond promptly and accurately.Decision making,therefore forecasting traffic flow provides a new governance idea for traffic congestion management.The paper first puts forward the background and significance of the research,literature review,and elaborates the research status of traffic grooming governance at home and abroad and the research status and development of intelligent traffic construction and traffic flow prediction technology at home and abroad.The second is to introduce the relevant knowledge of urban congestion theory,namely the definition and judgment of urban traffic congestion and the evaluation of traffic status.Introduce the relevant theoretical knowledge of artificial intelligence,involving machine learning principles,support vector machine model theory,data mining theory,etc.,and provide theoretical basis for the next step of building models,data mining and processing,and model analysis.The third is to analyze the current situation and causes of traffic congestion in the main urban area of Chongqing.Through on-the-spot investigation,data collection,and objective data to reflect the current situation of urban traffic congestion,specific analysis of population density,mode of travel,road construction,vehicle ownership,road network characteristics,motor vehicle and public transportation development status in Chongqing’s main urban area Congestion status and causes.The fourth is the establishment of models and data mining.Select to collect past vehicle data information for traffic jams on some important sections of the main urban area of Chongqing.Then,according to the relevant knowledge of data processing,data processing and data correction are performed.Using Python language programming for data mining and support vector machine analysis,the predicted traffic state is finally obtained,and the forecast information of urban road traffic flow in different time periods is obtained.The fifth is to specifically introduce the role of traffic flow prediction technology in traffic congestion management in the main urban area of Chongqing.From the application of urban road traffic management,service and organization,the significance of controlling traffic congestion is expounded,and the important role of traffic management department in strengthening the construction of modern traffic command management system is put forward.