Research On Urban Traffic Congestion Propagation Model On Multilayer Coupled Networks

With the rapid development of urbanization,traffic congestion has become one of the urgent problems to be solved in major cities in the world.Faced with the demand of high-efficiency,convenient and comfortable traffic travel of urban residents,the increasingly serious urban traffic jam not only brings inconvenience to people's daily life,but also severely restricts the construction and development of the city and reduces the efficiency of urban development.In order to prevent and effectively analyze the problem of urban traffic jam in time,establishing a scientific and reasonable model of urban traffic jam has become a hot issue urgently needed in the field of urban traffic management and intelligent transportation.Urban transport system is a complex giant system composed of people,vehicles,roads and traffic environment.The rise of complex networks provides a new perspective for studying urban traffic congestion.However,when applying the theory of complex network to study the problem of urban traffic jam,most of the existing researches only focus on the actual characteristics of the traffic network,and do not consider the impact of human behavior and information transmission on traffic congestion.Therefore,based on the theory of complex networks,this paper comprehensively considers the influence of information dissemination,human behavior and urban traffic routes,and thus abstractly obtains multi-types of networks such as information dissemination networks,personal social networks and road transport networks.By coupling these networks,the model of urban traffic jam propagation based on multi-layer network is constructed to reveal the characteristics of urban traffic jam propagation.The main research and achievements include:?1?A warning information and traffic congestion competing spreading processes model was proposed.In this model,we constructed a multiplex network with road intersections or sites to analyze the interplay between information spreading and traffic congestion spreading.Firstly,we considered the effect of the surrounding nodes and proposed an improved SIS model.Then,based on the improved SIS model,we used the method of state transition probability to study the competing spreading processes on multiplex network which is mentioned above.Finally,using the Monte Carlo method,we analyzed and simulated the traffic congestion threshold in both homogeneous network and heterogeneous network.The study has found and proved that the process of traffic congestion is not only related to the congestion propagation threshold and dissipation threshold.At the same time,to a certain extent,the dissemination of warning information in the network can reduce the spread of traffic congestion,thereby reducing the scale of the outbreak of congestion^[14].?2?Measuring and analyzing influential nodes in geo-social networks.According to the actual LBSN data,firstly,we proposed a geo-social multilayer with two layers including the online social network and geographic co-location weighted network.In this multilayer networks,the mutual user influence was calculated.Then,by comparing and analyzing the same influential spreaders on geo-social network,we seeked a possible way to find influential nodes by combing social relationships and individual behaviors.Finally,Susceptible–Infected–Recovered?SIR?model was used to evaluate the ability to spread nodes.The experimental results show that our method is effective for detecting the node influence and the geographic influence is stronger than social influence.The research content of this chapter lays the foundation for studying traffic congestion propagation model based on supernetwork.?3?A traffic congestion propagation model was proposed.Based on the above researches,firstly,we established a three-layer supernetwork model consisting of“social-information-congestion”.Based on this model,three kinds of network characteristics are extracted including users'social influence,road network structure characteristics and information dissemination characteristics.Then combined with these extracted network characteristics,by referring warning information and traffic congestion competing spreading model,we calculated the nodes'state transition probability in different traffic congestion propagation process.Finally,we presented a traffic congestion propagation model based on mean-field theory by introducing the concept of supernetwork.Experimental results showed that our model can inhibit the spread of traffic congestion.Moreover,the dissemination of warning information will increase the critical value of traffic congestion propagation.Therefore,the proposed model can comprehend the principle of warning information and traffic congestion competing spreading model under the influence of social networks.