Research On The Impact Of Accidental Events On MFD And The Control Of Urban Area Boundaries

With the rapid growth of car ownership in China,the urban traffic road network carries huge traffic demand.When the traffic demand is far greater than the road network's tolerance,the road network will be congested.If there is no timely and correct traffic control,the congestion will be It will continue to spread and drift until traffic is rampant.In recent years,many domestic and foreign scholars have begun to use the Macroscopic Fundamental Diagram(MFD)to design the boundary controller to adjust the traffic volume of the entire road network to maximize the road network capacity.At present,the existing MFD-based boundary control methods are mostly limited to a single control algorithm of a fixed scene.However,the realistic urban transportation system is highly random and uncertain.The MFD model parameters of the regional road network are difficult to be calibrated and susceptible to incidents.The traditional iterative learning boundary control scheme does not consider the impact of accidental events on the MFD model changes.Tracking the stability of the expected vehicle network cumulative vehicle number curve in different scenarios,and the tracking speed is slow,it is difficult to meet the requirements of the actual traffic system for the adaptive and effective control of the road network boundary control.Therefore,in view of the above two aspects,the research ideas of this paper are proposed.The main research contents are as follows:First,the research status,basic theory and principle of MFD are expounded.Aiming at the problem that the current MFD-based traffic boundary control ignores the impact of sporadic events on the model,the impact of accidental events on the MFD model is summarized by analyzing the traffic speed and flow changes of the actual accidents and reviewing a large number of documents.Finally,based on the MFD data of Yokohama,Japan,two trends of macroscopic basic maps under accidental events are simulated by simulation.Secondly,for the occurrence of sporadic events in the time-varying road network traffic system,the traditional single iterative learning boundary control scheme has weaker ability to track the expected curve and the error convergence effect is not good.The fuzzy control algorithm and iterative learning control are used.In combination,the robustness of traditional iterative learning control is improved.Finally,under various scene simulation experiments,the fuzzy iterative learning boundary control is used to adaptively track the expected curve under the disturbance of the spuriousevent to the road network model,and has good stability.Thirdly,considering that the core of the fuzzy control algorithm is based on the control rules summarized by expert experience and domain knowledge,in the highly random and uncertain real traffic,it is difficult to obtain accurate fuzzy rules under different sporadic events.Secondly,in the traffic control,the response speed of the control scheme has extremely high requirements,and the roughness of the fuzzy control rules is incomplete,which affects the convergence speed to varying degrees.In view of the above problems,the paper adds BP neural network to learn and adjust the gain based on iterative control.It can not only adaptively track the expected curve under sporadic events,but also improve the error convergence speed of the system.Through simulation comparison,it is verified that the improved boundary control scheme is better.