OD Matrix Estimation Strategy And Its Applications In Traffic Simulation Systems

With the development of the social economy and the speed-up of the urbanization and motorization, traffic jam, traffic accident, environment pollution and energy sources scarcity have been the common problems faced by all the countries in the world. Not only the developed countries but also the developing countries are all enduring the traffic problems. The traditional method resolving the traffic problems is to build roads, but the space of constructing roads has become less and less for anyone country. In addition, the traffic system is a complex and huge system; it is difficult to resolve the problem in essence by only considering the roads. Consequently, the Intelligent Transportation Systems (ITS), which systematically consider traffic basic establishment, traffic means of delivery and traffic participants, emerges as the times require. Generally, ITS include road traffic management system, the planning and design of the entire transportation system and the intelligentization of the transportation management. Traffic planning, traffic management and traffic control all cannot be separated from Origin-Destination (OD) matrices, namely, people needs to know the traffic demands on the road network. In addition, OD matrices are also the most direct and reliable input data for traffic simulation system. Traditional method obtaining OD matrices is to have a spot check in large-scale, but this way needs a vast expense and its organization is very difficult. With the popularization of the traffic monitor and control systems, the information of traffic flows could be obtained easily. As a result, estimating OD matrices by road traffic flows are one of the most economic and feasible methods to obtain OD matrices.For the ordinary urban networks, estimating OD matrix by road traffic flows mainly includes several steps as follows: checking and measuring road flow and obtaining apriori information; procuring road network characters and traffic assignment matrix; estimating OD matrix according to special estimate models. In the above steps, the main factors influencing the precision of OD estimation are: the accuracy of the estimate model, reliability of apriori information, accuracy of road flow checked and measured, and the rationality of the traffic assignment method. In addition, the solution to the model is also a problem worthy of discussion and the feasibility, simplicity and convenience of the solution influence the applicability of the model. Based on the maximum-entropy model and aimed at the OD estimation having the best precision, the solution to the model, traffic counting locations and obtaining traffic assignment matrix are discussed in detail in this dissertation with considering the several key steps ofOD matrix estimation. And then with the application example of the Simulation and Analysis System for Urban Mixed Traffic (SASUMT) designed by Zhejiang University, the input data are supplied for the simulation system by the OD estimation method. The maim research work is summarized as follows:1. Based on the maximum-entropy model estimating OD matrix, the solution by Genetic Algorithm (GA) is proposed. Firstly, the reasoning process and the theory basis of the maximum-entropy model are analyzed. According to the characteristic of the maximum-entropy model, the model is transformed into non-linear equations to resolve by the introduction of the elementary method resolving optimization problem with restrictions——Lagrange multiplier method. Because of the shortcomings of the traditional method——Newton's method, the calculation method of OD matrix by Genetic Algorithm (GA) is put forward. In this method, the decision-making variables of GA are the unknown quantities of the non-linear equations, the target function for optimization is the minimum of the root-mean-square error between the computational value on the left and the real value on the right in equations, and the initial value is generated randomly in the feasible fields of the decision-making variables. The example shows that GA has the better robustness, and overcomes the disadvantages of the Newton's method that strictly depends on initial values, doesn't easily converge and needs to calculate inverse matrices. When the initial values are far from the real values, there are more probabilities solving OD matrix successfully by GA than by Newton's method. Finally, the feasibility and reliability of GA method in solving OD matrix estimation models based on single-objective optimization are proved.2. In allusion to the problem how to determine the optimal number and locations of traffic counting points in estimating OD matrix by road flows, the improved location rules for traffic counting points are proposed according to the concept of maximum possible relative error: path covering rule and minimal traffic counting point rule. A corresponding integer programming model for determining the locations of traffic counting points satisfying these rules is established; and the approaches by GA for large-scale networks are proposed. The instance analysis shows that the OD matrix estimated by the number and locations of traffic counting points calculated from the above model can satisfy the precision demand. Compared with the other models for traffic counting point locations, this model can improve the precision of OD matrix estimated as soon as possible and at the same time, it can economize the costs of the traffic counting point locations. In addition, the model proposed in this dissertation has simple and explicit principle and can be readily calculated. It doesn't need apriori OD matrix and traffic assignment matrix, and consequently the influences ofdifferent errors are decreased. Compared with the former models, it has the better applicability.3. According as that traffic assignment matrix changes with the OD matrix in congested networks, the method in which OD matrix estimation and traffic assignment are performed alternately is brought forward. The estimation process based on different traffic assignment model and algorithm is proposed through the analyses to the circumstances with apriori OD matrix or not. When there is apriori OD matrix, equilibrium assignment is adopted in congested networks, and assignment matrix and OD matrix are correct repeated combining the process of OD estimation until the observed road traffic flow is reproduced. And without apriori OD matrix, probability assignment model is adopted to obtain the initial traffic assignment matrix. The apriori OD matrix for equilibrium assignment is procured through OD matrix estimation using the initial assignment matrix and observed traffic flow. Then OD matrix estimation is carried out according to the case where there is apriori OD matrix, and till the assignment flows are consistent with the observed flows. Finally, the estimating process and result in two circumstances are analyzed in an actual example. For the circumstance without apriori OD matrix, the estimation results of the method obtaining initial OD matrix in probability load means are compared with that of the method obtaining initial OD matrix in all-or-nothing means, and the feasibility and reliability of the former proposed in this dissertation are proved.4. The elementary function, the frame and the main models of the Simulation and Analysis System for Urban Mixed Traffic (SASUMT) partly designed by the author are discussed. The OD matrix estimation software designed for the traffic demand model, which can provide not only the data of crossing turning flows, but also the data of road network OD matrix for SASUMT in combination with traffic assignment software, is introduced. In addition, as a relatively independent module, the OD matrix estimation software can supply basic OD distribution forecast for traffic planning and traffic management. Finally, the reliability of the OD estimate result is proved through the simulating analyses of real network applied in SASUMT, and thereby OD matrix estimation can supply convenient, reliable input data for the application of traffic simulation systems.5. Finally, the work of this dissertation is summarized and the prospect of further research in OD matrix estimation is also discussed.