Urban Traffic Congestion Dispersion Research In Advance Under The Background Of IOT Technology

In recent years，with the development of urbanization in our country，vehicleholdings have increased rapidly and traffic jams have become a matter of commonoccurrence. The issues of high-frequency traffic accidents and urban traffic congestionhave grown into an increasingly concern. Problems caused by jam like prolongeddriving time，increased operating costs，traffic accident，air pollution， and noisepollution have impact on related industries meanwhile with dragging down the overallefficiency of urban economy. The ratio of direct economic loss in GDP， caused bytraffic congestion in our country， has been rising year by year. NationalMedium-to-Long Range Program for Scientific and Technological Development(2006-2020) has put that issues like to relieve the tension of urban traffic congestion，traffic energy consumption，environment pollution，traffic safety etc. should beresolved. Traffic congestion is a prominent manifestation of a series of urban trafficproblems. Therefore，to relieve and prevent for traffic congestion is the top priority ofcity development in our country.Traffic flow in urban areas，with feature of strong-nonlinearity and time-varying，is complicated and always in quick change. The traditional congestion ex post directmethod，because of poor adaptivity and without consideration of traffic flowcorrelation，is unable to cope with that complexity; as a result，the effect of congestionguidance is bad. This paper probes into system optimization-oriented urbancongestion ex ante guidance with dynamic strategy，based on principle of modelprediction，dynamic calculation，feedback online，and ex ante guidance.The wide application of Internet of Things technology makes possible thereal-time information perception and collection and promotes the systematicperception to a large extent. In the field of communication and transportation，information transmission and processing technology has been gradually applied inadministrative decision-making of urban traffic and has an effect on traffic control.Furthermore，the information availability at anyplace and anytime has influence ondriver’s driving decision，which changes driving group’s decision-making. The use ofadvanced information technology and transportation technology greatly eases trafficcongestion，improves city and highway traffic system efficiency and quality. Throughinteraction between traffic demand and traffic supply，the ongoing and to-be traffic condition can be estimated. In line with the latest traffic information， thecorresponding traffic management control strategy can be formulated to ensure theordering and high efficiency of traffic flow and to exert the maximum function of roadsystem. Our government has taken the intelligent transportation system as the majordirection of China’s future transportation development. And in "twelfth five-year"transportation planning， Ministry of Transport has already started two key projects ANew Generation of Intelligent Transportation System Development Strategy andApplication of Internet of Thing in Modern Transportation Strategy.The main work and findings of this paper are as follows:There are few literatures on modern information technology influence on urbantransport complex network and its mechanism， neither the further research on trafficflow evolvement rule. While it is necessary to make an intensive study on factors andmechanism in improving overall urban transport network efficiency. The complexityof urban road network requires a dynamic resolution. And a dynamic prediction oftraffic flow is a key point to realize dynamic urban traffic assignment and guidance.Based on the understanding of complexity of urban road network，this paperdecomposes the road network’s quantitative information through physical，data，andfunctions aspects，which laid a basis for the traffic section flow evolutionary modelthat the paper makes. From this model，some features and rules that govern trafficflow evolution have been revealed. The matrix model of network traffic flowevolution can be seen as establishment of overall dynamic model and real-timeprediction of traffic flow，which gives support to the scientific decision-making forurban traffic network optimization and traffic guidance.System transmit traffic information to the driver take advantage of the IOTintelligent terminal，induce its behavior，to realize intelligent traffic network flowassignment. Because of Traffic inducing information，the driver’s action would be toointense，cause serious traffic shift phenomenon and induction strategy is invalid.thedrivers route choice under Traffic information is evolution process among trafficmanagers，the drivers. by means of a long-term dynamic game，drivers decide theirown behavior based on the maximum travel benefit，the structure of path selectioncan achieve evolutionary stable status under certain condition. This paper have studieddrivers swarm behavior decision-making rule under the complete traffic flowinformation，and studied the science decision of traffic administrative department. Byanalyzing the driver evolution process of the game and path selection strategy basedon different information inducing，established the drivers’ route choice evolutionary game model，get the evolutionary stable strategy in the drivers’ route choice，formulate the scientific traffic guidance strategy and its effect evaluation， etc. Thispaper have studied the organic combination of guidance and control means， Proposeguidance and control method to implement congestion for invalid problem of theinduction method in supersaturated sections.Through IOT-based network efficiency evaluation model， this paper makes acomparison of traffic network efficiency in ex ante and ex post guidance. The paperholds that the scientific measurement，calculation， and prediction of traffic networkefficiency are the key points to verify the optimization performance of traffic network.According to complex network theory， traffic equilibrium theory， and macroscopictraffic flow theory，this paper constructs models and makes measurement andestimation on transport network transmission and operating efficiency from the angleof efficiency of network static transmission and driving cost. The networkingoperating efficiency is measured from driver’s time cost.