Research On Model For Traffic Bottleneck Guidance Coordinated With Traffic Control Based On Cloud Computing

With the development of the city and the improvement of living standard, carownership is growing rapidly, which bring tremendous pressure to the urban trafficsystem. At the same time, traffic congestion, traffic accidents and environmentalpollution appeared frequently, these phenomenons have seriously affected theefficiency of people traveling and quality of life. When the road capacity can’t meetthe demand of traffic, and there is no way to get treatment timely, the trafficcongestion occurs. Urban road network is composed of intersections and sections.Because of the irrationality between the topology of network and the layout of trafficfacilities and the unceasing change of traffic flow, there are many fixed trafficbottlenecks and dynamic traffic bottlenecks in network. With the traffic demandincreasing, road capacity can’t meet its needs. When the traffic demand increases to acertain extent, traffic congestion will occur at the transport bottlenecks first. Thetraffic congestion may travel around or spread, which forms a "Domino Effect". Thatis the reason of a wide range of traffic congestion.Traffic bottleneck is the weak link of urban road network system and the root ofthe urban traffic congestion. If not control traffic bottlenecks from the source, willlead to traffic congestion spreading rapidly, even creating congestion loop, which isthe lock phenomena. So the study of the management method traffic bottleneck is ofimportant significance.In the process of traffic bottlenecks management, the singletraffic control or traffic guidance for the traffic bottleneck sections realizes themanagement of the traffic flow just in time or space alone, which can’t achieve verygood effect, so the model of traffic bottleneck control with guidance is imperative. Inthe process of studing the model of traffic bottleneck control with guidance, the firstto identify the traffic bottleneck, then to predict dynamic traffic bottlenecks, and tosolve the collaborative optimization model of the traffic bottleneck control withguidance. These links are inseparable from the efficient handling of trafficinformation. Since the date of birth, cloud computing is attention by the researchers invarious fields. The ability of quick service, fast processing capacity and elasticcomputing ability of cloud computing provides the opportunity for its development inthe field of traffic information processing. Therefore, on the basis of using the cloudcomputing technology to analyze and deal with vast amounts of traffic dataintelligently, studing the coordination model traffic bottleneck control with guidancebased on cloud computing provides the theoretical basis and the technical support forthe governance of traffic bottlenecks and the reference of development and application of cloud computing in the field of transportation.The collaborative model of the traffic bottleneck control with guidance based oncloud computing are studied in this paper. On the basis of the framework of the trafficbottleneck control and guidance system based on cloud computing, first study theidentification method of traffic bottlenecks based on MapReduce, including fixedtraffic bottleneck identification and dynamic traffic bottleneck identification, and thenstudy the prediction method of the dynamic traffic bottleneck based on MapReduce,and finally study the collaborative optimization model of traffic bottleneck controlwith guidance based on cloud computing, and take a parallel algorithm to solve thecollaborative model based on MapReduce and genetic algorithm, so as to improve theprocessing efficiency of traffic information. It is better to meet real-time demand ofthe user for the traffic bottleneck control and guidance. The concrete research contentis as follows:(1) The framework of the traffic bottleneck control and guidance system basedon cloud computingFirst, take an in-depth analysis on the necessity of cloud computing beingapplied to intelligent transportation system, including analysis of cloud demand ofIntelligent Transportation System, research on the cloud computing technology andHadoop, which is the open source implementation framework of Google CloudComputing, and build the basic framework of intelligent transportation system basedon cloud computing. Then, analyze several key problems of the traffic bottleneckcontrol and guidance system, and expound the necessity of cloud computingapplication. Finally, built the framework of the traffic bottleneck control and guidancesystem based on cloud computing.(2) Identification method of traffic bottlenecks based on MapReduce andK-meansFrom the influence factors of the fixed transport bottlenecks and the dynamictraffic bottleneck, select the appropriate identification index of fixed transportbottlenecks and dynamic traffic bottleneck. In the full study of MapReduce parallelprogramming model and K-means clustering algorithm, get the parallel processingway of K-means algorithm based on MapReduce. Finally, propose identificationmethods of the fixed traffic bottleneck and the dynamic traffic bottleneck based onMapReduce and K-means, and the feasibility and effectiveness of the proposedmethod are verified through the instance.(3) Prediction method of the dynamic traffic bottleneck based on MapReduceand GA-SVMIn order to improve the accuracy of short-term traffic flow prediction, fullyconsidering the spatial and temporal correlation of traffic flow. The space and timecorrelation of traffic flow is analyzed using the method of system cluster analysis.Then study support vector machine (SVM) model, and put forward a kind of parameters optimization algorithm of SVM based on MapReduce and geneticalgorithm (GA). Apply optimized SVM model to the short-term traffic flow prediction,and get traffic flow parameters of the future time. Finally, input the traffic parametersto dynamic traffic bottleneck identification algorithm based on MapReduce. Thedynamic traffic bottleneck prediction is realized. The feasibility and advantage of thismethod is verified by examples.(4) Coordination model of traffic bottleneck control and guidance based on cloudcomputingBased on modeling ideas of the model of traffic bottleneck control with guidance,with "sub-optimization of system" for the principle, and give full consideration to theparticularity of the traffic bottleneck. When the queue length is greater than zero,build the collaborative optimization model of traffic bottleneck control and guidance.Solve collaborative optimization model using MapReduce and parallel geneticalgorithm. Finally, prove the effectiveness of the proposed collaborative model andthe feasibility and efficiency of this algorithm through the simulation experiments.