Dynamic Modeling And Stability Analysis Of Traffic Flow About Multi-Information Feedback Effect Under Intelligent Transportation Environment

In recent years,with the rapid development of economy and the deepening of urbanization,the number of urban motor vehicles continues to rise,resulting in traffic congestion problem becoming more and more prominent.At present,with the wide application of Intelligent Transportation System(for short,ITS)and the rapid development of vehicular networking technology,drivers can receive the running status information of multiple vehicles in front of the road in real time through Vehicle-to-Vehicle(for short,V2V)communication equipment.The feedback information will have an important impact on the driver's decision-making and driving behavior,resulting in complex dynamic characteristics of traffic flow.Therefore,under the background of the development of ITS,it is of great practical significance and application value to carry out traffic flow dynamics modeling and stability analysis of multi-information feedback effect,reveal the formation mechanism of traffic congestion and put forward effective strategies to alleviate traffic congestion.On the basis of the existing microscopic and macroscopic models of traffic flow and the ITS environment,we consider the driver's behavior characteristics(e.g.,self-stable driving behavior),average optimized density difference,average flow difference and mean expected velocity field effect.Some multi-information feedback effect of the traffic flow dynamic model are established,and the corresponding theoretical analysis and numerical simulation are carried out.We focus on the stability of traffic flow,nonlinear density wave and the evolution law of traffic congestion,which can provide some theoretical basis and scientific suggestions for the traffic control department.The main work of this paper is as follows:?.Under the ITS environment,the influence of the coupling the effects of driver's memory and headway difference on traffic flow stability is studied from a microscopic perspective.An extended self-stable driving traffic flow following model is established.The theoretical analysis and numerical simulation of the model show that the multi-information feedback coupled with the headway difference and driver's memory effect can effectively alleviate traffic congestion and enhance the stability of the traffic system.The headway difference effect has a more stabilizing effect on the traffic system.In addition,the driver's memory time has no effect on the vehicle's start time.?.Considering the influence of driver's backward looking on the traffic system at the entrance and exit of ramp from the macroscopic level,an improved ramp lattice hydrodynamics model is proposed under the ITS environment.The influence of driver's“backward looking” on the stability of ramp traffic flow is studied.The stability condition of the new model is obtained by stability theory analysis,and the m Kd V equation is derived by nonlinear analysis.Theoretical analysis and numerical simulation results show that considering the driver's backward looking effect can improve the stability of the off-ramp traffic system,but the stability of on-ramp traffic system will be worsen on the main road.The research can provide some theoretical basis for the ramp construction planning of ITS.?.Based on the application of ITS,considering the influence of average optimized density difference effect on traffic flow,an improved one-lane lattice hydrodynamics model is proposed.We focus on the multi-information feedback of preceding vehicles group on the stability of traffic system.Through the theoretical analysis and numerical simulation,we find that the average optimized density difference effect can ease traffic congestion.when considering the density information of more vehicles ahead,the traffic congestion can be effectively alleviated and the traffic system can be more stable.This research can provide some theoretical basis for the development direction of density information feedback in ITS.?.Based on ITS's application,two three-lane lattice hydrodynamics models are proposed by considering the average flow difference effect and the mean expected velocity field effect,respectively.The influence of these two effects on the stability of multi-lane traffic system are studied by combining theoretical analysis with numerical simulation.The results show the average flow difference effect and the mean expected velocity field effect play a positive role in improving the stability of traffic system.Moreover,the more feedback information of the lattice numbers ahead,the traffic congestion will be effectively alleviated and the traffic system will be more stable.In addition,the three-lane system has the strongest stability due to the lane changing behavior of vehicles in the neighboring and next-neighbor lanes.In conclusion,considering driver's behavior characteristics(e.g.,self-stable driving)and factors such as the average optimal density difference,average flow difference and mean expected velocity field of preceding vehicles in the ITS environment,can enhance the stability of traffic system operation to some extent and achieve the purpose of alleviating traffic congestion.The above studies can provide useful reference for the operation and development of urban intelligent transportation and the intelligent transportation research in the era of big data and artificial intelligence.Finally,the main research work is summarized and the future research work is prospected.