Dynamic Evolution Mechanism Of Urban Transport-Land Use Based On Self-Organizing Theory

The rapid development of national economy facilitates the process of urbanization and mechanization, expands the urban scale and increases the travel demand of urban residents. Obviously, the urban transport contributes a lot to the development of society, however, it also generates many problems. Traffic congestion in metropolises is a big headache for governments and transport planners all over the world. The relationship between urban transport and land use can be regarded as the link between "source" and "stream". It has been proved that the coordinate development between land use and urban transport is a prerequisite for resolving the traffic problem fundamentally and realizing the intensification of land use. Meanwhile, it is a key to the sustainable urban development.This paper focuses on "Urban Transport-Land Use"(UT-LU) and conducts a study on the evolution mechanism of the UT-LU from the macro and micro perspectives based on Self-Organization Theory. From the macro perspective, the Dissipative Structure Theory, the Synergetics Theory, the Hypercycle Theory, the Catastrophe Theory, the Chaos Theory and the Fractal Theory will be used to reveal the UT-LU's evolution condition, evolution power, evolution binding form, evolution approach evolution prospect, evolution Structural Complexity, respectively. And then the paper takes Beijing as an example to examine the applicability of the macro perspective of Self-Organization Theory in the UT-LU.From the micro perspective, the Multi-Agent technology is adopted to analyze the micro self-organizing evolution mechanism of each component, such as travel demand, transport network status, residential location choice, real estate price, etc., in the UT-LU. Then establish the relationship among these components according to sequencing. By mutually calling these components, the self-organizing evolution of UT-LU can be implemented. In addition, the micro-data synthesis method and simulation platform SelfSim, which are used for analyzing the evolution of UT-LU, are also studied. The details about the above study are as follows:(1) Micro-data Synthesis Method. Both population and initial plans are the basic data required by the study on micro-evolution of UT-LU. Since the traditional population synthesis method doesn't take the initial household weights into consideration and its optimization objective ignores the discrete extent of average error of control variables, a new heuristic algorithm is proposed to overcome the limitations mentioned above. In addition, the approach of generating initial plan is employed to generate an initial plan for each agent in SelfSim. And the Utility Maximization Theory and Genetic Algorithm are integrated into the approach. The initial plan will be treated as an initial solution to the plan adjustment. Finally, take Baoding as an example, the parameters setting of population synthesis method and the approach of generating initial plan is discussed.(2) Multi-Agent-Based Coupled Model of Activity Schedule and Traffic Assignment. Since the interaction between travel demand and transport network status, both of them are modeled together in the study of micro self-organizing evolution of UT-LU. After the synthetic population and initial plan are both input into the coupled model, the final plan of each agent and transport network status will be output. Actually, the Multi-Agent technology here is used as a tool to simulate the interaction between travel demand and transport network status. The modeling contains defining the agent and simulation environment, as well as establishing the core part of simulation. As for the core part of simulation, it involves the sub-modules of execution, scoring and reschedule. These three sub-modules form a cycle in calling, and then the final plan and transport network status are output after certain iterations. Finally, the coupled model is applied to Baoding and the results show that the forecasting accuracy of the model meets the requirements of application.(3) Multi-Agent-Based Coupled Model of Residential Location Choice and Real Estate Price. Since the interaction between residential location choice and real estate price is in UT-LU evolution, residential location choice and real estate price are modeled together. The Multi-Agent technology is employed to model the Household Agents'residential selection behavior, Owner Agent's behavior of updating price and the communication behavior between Household Agent and Owner Agent. The Household Agents'selection of their houses and the Owner Agents'pricing of their houses will finalize through their communication. Here the Utility Maximization Theory and the Prospect Theory are both employed to model the residential choice behavior of the Household Agents. Meanwhile, the Multi-Agent-Based Micro-Simulation Accessibility Model is developed to calculate the house accessibility, which is an influencing factor in Household Agents'selection of houses. Here, the coupled model is also applied to Baoding and the forecasting results demonstrate that the model gains a satisfactory forecasting ability. (4) Population Development Model. The population development is one of the crucial factors for promoting the evolution of UT-LU. The Population Development Model attempts to model the status and features of the population which are closely related to UT-LU evolution. This model mainly includes Birth Model, Education Model, Work Model, Marry Model, Death Model, Car Ownership Model, and Income Model.(5) Simulation Platform of SelfSim. By integrating all the models of key component mentioned above, the SelfSim is developed for the analysis of UT-LU evolution. Finally, the short-term evolution of UT-LU of Baoding from2008to2013is simulated and its results show that SelfSim has the high forecasting ability in a few sides.