Uncertainty Modeling Of User Equilibrium Assignment Based On Identity Recognition Data

Under the background of increasingly serious urban traffic congestion,urban traffic management and control are effective measures to alleviate traffic congestion.Regional traffic signal control and traffic assignment are interactional and mutually restrictive.Because of the uncertainties in urban traffic system,it is necessary to consider the uncertainties in traffic assignment procedure to improve the robustness of urban traffic signal control system.Therefore,based on the identity recognition data,i.e.Radio Frequency Identification(RFID)data,and considering the impacts of uncertainties in dynamic traffic demand on traffic assignment procedure,uncertainty modeling of user equilibrium assignment based on interval estimation method is proposed in this thesis.The main research contents of this thesis are as follows:Firstly,the research progress of identity recognition data,dynamic traffic demand uncertainty and traffic assignment uncertainty at home and abroad are summarized.Advantages of RFID data over license plate recognition data,and advantages of interval estimation method over stochastic programming and fuzzy programming method in the study of uncertainties are obtained,which determine the research ideas of this thesis.Secondly,the framework of dynamic traffic demand uncertainty estimation is constructed.Following this framework,the estimation results of offline dynamic OD flows based on RFID data are taken as the true value.To estimate OD flow level,the state space model is established and solved by Kalman filtering algorithm.On this basis,the GARCH model is applied to model the variance of OD flow estimation results,and the confidence interval of OD flow is calculated under a given confidence level.To evaluate the confidence interval,two measures are applied,i.e.,the confidence interval width-to-flow ratio(R)and the confidence interval kickoff percentage(KP).Then,modeling framework for the uncertainty of user equilibrium assignment is constructed.To obtain the confidence interval of link flow,user equilibrium assignment model is established which is solved by Frank-Wolf algorithm,and the upper and lower bounds of OD flow for each interval are served as inputs,respectively.Same as above,the confidence interval width-to-flow ratio(R)and the confidence interval kickoff percentage(KP)are applied to evaluate the confidence interval.Finally,a road network in Nanjing,China is selected as the research object.According to the calculation process of dynamic traffic demand uncertainty,the dynamic OD flow level is estimated.On this basis,the confidence intervals of dynamic OD flow and user equilibrium assignment are obtained under 95%,90% and 85% confidence level.Results demonstrate that the confidence intervals result of link flows in road network can be obtained with the method proposed in this thesis,but the interval widths are wide,and the method still needs further improvement.In this thesis,interval estimation method is applied to the analyses of dynamic traffic demand uncertainty and traffic assignment uncertainty,which are significant to enhance the robustness of urban traffic control system.