Dynamic Modeling And Numerical Simulation Of Traffic Flow Based On Intelligent Transportation Information Feedback

In recent years,with the continuous improvement of people’s living standards and the acceleration of urbanization,the number of urban motor vehicles has increased rapidly.The problem of road traffic congestion is becoming more and more prominent,which brings great pressure to the urban road traffic system and faces a severe test.At present,with the development of 5G,AI,Internet of things and other technologies,the intelligent transportation system(for short,ITS)has become the mainstream direction of the development of urban intelligent transportation system.It has an important impact on the control of urban traffic.Therefore,the dynamics modeling and stability analysis of traffic flow in ITS environment has important practical significance and engineering application value.Based on the existing microscopic and macroscopic models of traffic flow and the intelligent transportation system,considering all kinds of traffic information(e.g.,the average expected velocity field,average density difference and average flow field of the vehicle group in front of the road,etc.)feedback and driver behavior characteristics(e.g.,memory effect and the expected effect),puts forward some improved traffic flow dynamic model,and the corresponding theoretical analysis and numerical simulation are carried out.Moreover,the study of nonlinear density sweep evolution in traffic flow can provide some theoretical basis for traffic management and control.The main work of this paper is as follows:I.From the microscopic point of view,an improved traffic flow car-following model is proposed by considering the incorporating the effects of driver’s memory and mean expected velocity field in ITS environment.The influence of the coupling effect on the stability of the traffic system is emphatically studied,and the evolution characteristics of traffic density waves are deeply discussed.The results show that the coupling of driver’s memory effect and average expected velocity field effect can effectively suppress traffic congestion and improve the stability of traffic system.In addition,compared with the two effects(the driver’s memory effect and the average expected velocity field effect),the average expected velocity field effect is more favorable to enhance the stability of the traffic system.The study can provide some theoretical basis for the safe driving of drivers and the construction planning of intelligent transportation system.II.From the macroscopic perspective,considering the average density difference and the average flow field coupling effect and considering traffic discontinuity and the effect of traffic flow density difference under ITS environment,respectively.The corresponding lattice hydrodynamic models of traffic flow(i.e.,single lane lattices hydrodynamic model of average density difference and average flow coupling effect and two-lane lattices hydrodynamic model of traffic discontinuity and density difference effect)are established,respectively.The effect of two kinds of information feedback related to density difference on traffic flow stability is studied.The results show that the average flow field effect can greatly enhance the stability of the traffic system,and the density difference effect can stabilize the traffic system in the discontinuous road system.Furthermore,the traffic system becomes more stable when more information about the road ahead is taken into account.The above research can provide some theoretical basis for the research of information feedback and control in the intelligent transportation system.III.Based on the application of ITS,two improved ramp lattice hydrodynamics models are established by considering the influence of the optimal average flow field effect and driver expectation effect on the traffic flow with on-ramp and off-ramp,respectively.And focus on the average traffic information feedback of vehicle group in front and the influence of driver’s expected behavior on the stability of traffic system.The stability analysis of the new model and the m Kd V equation describing the evolution of traffic congestion are obtained by linear and nonlinear analysis,respectively.The results show that the stability of ramp traffic system can be effectively enhanced and the traffic congestion can be restrained by considering the optimal average flow field effect and driver’s expected effect with the on-ramp and off-ramp.In a word,in the ITS environment,considering the feedback of traffic information such as the average expected velocity field,average density and average flow of the vehicle group in front of the road,the driver’s behavioral characteristics and other factors,which can relieve traffic congestion and improve the stability of the traffic system.Our research can provide some theoretical basis for urban intelligent transportation planning and intelligent vehicle driving strategy design.Finally,the thesis is summarized and the future research work is prospected.