Location Selection Of Urban Road Traffic Resoures Based On Soft Computing

Urban traffic problem is one of the hot issues of social concern. The rapid development of soft computing technology, emerging various models and comprehensive analysis as well as new theories and research results has shown great power and development potential in practical theoretical researches and engineering applications. Use soft computing technology to carry out scientific location selection to enable limited urban road traffic to achieve maximum performance, thus play the role of further easing traffic contradictions, which is the main issue of this paper.On the basis of extensively collecting and reading the latest literature and achievements concerning location selection—allocation and soft computing theory and method at home and abroad, this paper has studied the basic idea, principle and basic theory of fuzzy logic, artificial neutral networks, generic algorithms and rough sets in soft computing theory, systematically researching the theory and methods of urban road traffic location selection. Targeting on the difficulties and uncertainties in the location selection of different urban road traffic resources, integrate the basic idea of soft computing idea into the strategy of urban road traffic resource location selection, organically combine these soft computing technologies on this basis, proposing the corresponding soft computing models and algorithms to improve the utilization and availability of urban road traffic resources, provide reasonable and scientific implementation methods, thereby promote the application of soft computing methods in the engineering of urban road traffic resource location selection. Analysis and simulation shows these methods can more effectively solve the problem of urban road traffic resource location selection. The main contents of this article are as follows:First, it carries out in-depth analysis on the severe situation faced by urban road traffic, pointing out the role and significance of road traffic resource location selection in intelligent transportation systems (ITS), on the basis of collecting and reading the existing location selection and allocation as well as soft computing technology theory, then makes detail introduction on the development, research and application of soft computing method, this paper proposes the idea of conducting urban road traffic resource selection based on soft computing method.Study the integration of soft computing method, based on the analysis of merits and demerits of common soft computing method, discuss the philosophical foundation, methodological foundation, integration principles and integration forms of soft computing method, researching the technology route of soft computing integration in this paper.Study the decision knowledge rule extraction in the research of urban road traffic resource location selection integrated by fuzzy logic, neutral networks and genetic algorithms. It firstly focuses on the integration of fuzzy logic and neutral network, discusses efficient transformation of urban road traffic location selection resource property or road traffic survey data to fuzzy rules with expert knowledge. The paper proposes genetic algorithm based NFS thought, realizing the transformation from data to information and from information to knowledge.Research the method of using gray neutral network prediction in solving small data, small samples, poor information and uncertainty in face of urban road traffic resource location selection; describe the basic theory of grey system and neutral network and analysis the main factor of transfer need amount for bus transfer hub selection. On this basis, the transfer need amount prediction solution model improving grey neutral network "whitening" parameters by genetic algorithm, based on the practical application, carry out simulation experiment study and comparative analysis, thus conduct in-depth study on the application of generic algorithm based grey neutral network improvement method.Study the model of independent variable dimensionality reduction and system prediction by surplus or numerous and jumbled modeling factors in urban road traffic resources location selection with the combination of multiple heterogeneous genetic algorithm and neural network. First, it makes an overall introduction of applying neutral network prediction theory and technology in urban road traffic resource location selection, analyzes the multi-factor and high-dimensional variables faced by rail transit route location selection, and then puts forward the factor screening and system prediction model method with the composite optimization of generic algorithm and BP neural network. Finally, conduct simulation research based on the practical application need data to realize the factor screening in urban rail transit lines, and make in-depth analysis on urban rail transit lines location selection.Study high-density urban parking facility locations selection based on the combination of rough set attribute reduction and GIS technology. Start from the analysis of high-density urban packing facility geographic information features, it proposes using the combination of rough set theory and GIS technology to get the model and algorithms of high-density urban parking facility location selection decision factors'attribute reduction. Make in-depth study on the attribute reduction by using mutual information fuzzy rough set to obtain the relative reduced decision rules, and then carry out evaluation and analysis on the decision classification in planning. With the actual simulation results, carry out assessment and analysis on research results.Some problems about location selection of urban road traffic resources that should be noted are discussed.Finally, make a summary of the full text, propose the researches requiring improvement and point out issues needing further study of urban road traffic resources location selection in terms of theory and application.