Traffic Flow Modeling And Congestion Control Based On The Driving Behavior Under The Car Networking Environment

As we known,the driver behavior and the traffic jam exists the closely linked,and the conclusion has been verified that optimizing the driving behavior can to reduce the occurrence of traffic jams,the traffic flow theory as the bridge that crosses from the relationship between traffic driving behavior and traffic jams,through the analysis of traffic flow modeling can obtain the key factors of influencing the traffic jam,meanwhile can reproduce the story of the formation of traffic congestion.Currently on the theoretical modeling analysis of traditional traffic flow is too simple,and ignored people as the main body contains important information driving factors,which makes analysis on the formation of a traffic jam is not enough clarity,qualitative inaccurate on the influence factors of traffic congestion,this make a lot of theoretical model on the design of control algorithm can not be implemented in the real case,even if the implementation effect is often lower than expected.with the development of intelligent transportation,which makes the vehicle not only can get the current vehicle information,but also be able to get the detailed information of the other vehicles on the road,this to ease traffic congestion and improve road service level to provide an effective tool.Car networking as a national key project supported by the "much starker choices-and graver consequences-in",is an important developing trend of the future intelligent transportation,therefore,how in the network environment,considering the driver’s behavior of concrete,analyzes the characteristic of traffic congestion,and offer some measures to inhibit congestion is more theoretical and practical significance.In this paper,based on the classical traffic flow theory and traffic congestion control method,considering the multiple effect of the time-varying delay time,variable sensitivity and the memory time,the driving behavior as the core,from the macro model and micro model to analysis respectively,the micro level focus on single lane drivers affected by time-varying delay time and the fuzzy sensitivity effect,furthermore,in order to enhance the stability of the model,the control algorithm is designed,in the macro level mainly considering the car following,overtaking and lane changing is three different circumstances analysis the driver affected by the optimal current change with memory and modeling analysis in ITS circumstance,using the method of linear and nonlinear stability on the theoretical analysis and numerical simulation,explore the driver in the car networking environment to consider the optimal flow changes in memory time difference under various nonlinear phenomena of traffic flow,the paper’s main work is as follows:First of all,from the micro level,taking into account the driver inevitably affected by the delay time,and the delay time is not like the rest of the scholars hypothesis is constant,based on the optimal velocity car following models and the coupled mapped car following model,using the control method,two class of car following model with variable delay time are proposed,and in order to suppress the traffic congestion,In this paper,the control of a class of feedback controllers is designed by means of the difference in speed between the front and the back of the vehicle,and the sufficient conditions that the existence of the feedback controller are given.The subsequent simulation example compared the evolution of velocity with the time under the controller or not,and proves that the controller in suppressing traffic congestion is effective.Second,the driver’s sensitivity is constant in previous research,while the actual survey shows that in different speed and headway between the driver’s sensitivity will exist slightly change with the velocity and headway,based on the VTD & OV model and the VTD & CM model,introducing the fuzzy control theory.The new fuzzy VTD & CM model and the fuzzy VTD & CM model are presented respectively,and the stability of the two types of car following models were studied separately.Based on the piecewise Lyapunov function method,the sufficient condition that the asymptotic stability of the new fuzzy car following model is given,when the stability condition is satisfied,the traffic congestion will not occur,reversely,the traffic congestion will appear,in order to suppress the traffic congestion,the control algorithm of the fuzzy VTD & OV model and the fuzzy VTD & CM model is designed,and the controller can be obtained through solving LMIs.Finally,a simulation example is given to demonstrate that the method in keeping the traffic stable and reducing the carbon dioxide emissions is effectively and feasible.Finally,from the macroscopic level,based on the optimal current information(OC: Optimal Current)provided by the car networking environment,considering the influence of the driver’s memory time on the driving process,in order to analysis the evolution of the traffic flow under the optimal current change with memory,based on the single lattice model,two lane lattice model considering the change lane behaviors,and the overtaking lattice model,we modeled in above case,and to analysis the effect of the optimal current change with memory in car following behaviors,lane changing and overtaking,and the macroscopic propagation of traffic congestion under the influence of the optimal current change with memory is explored mechanism.Considering the driver’s will through the flow different between the this moment and last moment to adjust the vehicle acceleration in next step,In this paper,based on the original Natagani model,a new lattice model with optimal current change with memory is presented,through the linear stability theory and the nonlinear theory we analysis the new lattice model,The former can obtain the linear stability condition,and the latter can obtains the kink-antikink solution,and to analyzed the propagation mechanism of the density wave in the critical point region.Finally,some numerical simulation shows that the increasing the memory time step and the sensitivity of the optimal current change with memory can improve the stability of the traffic flow.However,the above lattice model merely describe the evolution of traffic flow in single lanes,and the models can not be used for the multi-lanes traffic flow with the lane changing behavior.In this paper,the considering the optimal current change with memory single lane lattice model is extended to the two lanes,and the new two lanes lattice model is established,similar to the single-lane study,the theoretical and numerical simulation of the traffic flow in the new model is carried out,the results show that increasing the memory and the sensitivity of the optimal current change with memory to enhance the traffic flow stability still feasible,and considering a certain lane changing more conducive to ease the traffic congestion,With the in-depth findings we found that the above model merely reflect "normal" driving conditions such as car following,lane changing,etc,not apply to "unconventional" driving conditions such as overtaking,In this paper,the optimal current change with memory is introduced into the Natagani overtaking lattice model.Meanwhile,the stability criterion of the model is obtained based on the linear stability theory.Then,the propagation evolution of the traffic jam near the critical point is obtained based on the nonlinear stability method.The results show that the size of the overtaking rate seriously affects the stability of the traffic flow.When the traffic overtaking rate is low(below a set threshold),the whole phase space is only divided into the stability region and the unsteady region,and the kink-antikink wave propagated backwards in the unsteady region,the kink-antikink wave can be obtained by solving the mKDV equation,with the driver’s memory time step increased,the traffic flow of the stability can be enhanced;when the overtaking rate is greater(over a certain threshold),the above-mentioned unstable area divided into two parts: the density wave characteristic region and the chaotic characteristic region,and the two regions show completely different morphology,in the density wave characteristic region,the unsteady traffic flow mainly evolves backwards in the periodic behavior,and in the chaotic feature area,the unsteady traffic shows a seemingly "disorganized" chaotic behavior.In summary,this article mainly through the car network platform,in order to analyze the relationship between time delay fuzzy sensitivity and memory time and traffic congestion in driving behavior,this paper mainly builds the macro traffic flow model and micro model,In the micro-traffic model,a feedback control algorithm is designed to improve the robustness of the traffic flow.The research results of this article can deepen the understanding of traffic congestion and the process of traffic congestion formation,to understand the impact of the driver’s own characteristics on the stability of the traffic flow.simultaneously,it can construct a new system and framework with the traffic flow congestion control in the vehicle network environment,enrich the research results of the previous traffic flow,open up new branches for the research of traffic flow,the design of the control algorithm for advanced intelligent driving system design to provide some guidance.