The Research Of Integrated Land Use And Transportation Models’ Application In Small And Medium-sized Cities

During the "Thirteen Fifth" period, with the development of new urbanization planning in China, small and medium-sized cities are still in the development period of expansion, their urbanization and motorization level will continue to improve, and their urban land use pattern and traffic structure will also have a profound change. In order to avoid embark the path of the past cities’ sprawling urban development, appearing problems between urban land use and transportation, attentions should be paid to the coordinated development of land use and transportation system in small and medium-sized cities, to explore positive interactions between the rational development of land resources and transportation system, as to achieve coordinated development of the cities’ economy and society. Therefore, this paper mainly studies how to realize the application of integrated land use and transportation models in small and medium cities, as to forecasting the land use and traffic development trend of small and medium cities.In this paper, on the basis of expounding the basic concepts and principles of the integrated land use and transport models, through analysis of the actual development of China’s small and medium-sized cities and comparison of the integrated land use and transport models, selected ITLUP model to study its application in small and medium-sized cities for research. Analyzed each block of the ITLUP model and their application method in small and medium-sized cities, determined the required input data of the model, and analyzed the limitations of the models’ application in small and medium-sized cities, by adding the feature index into the model, optimized the land use module’s allocation algorithm, enhanced the practicability of the model in small and medium-sized cities. Based on investigating the factors influence model uncertainty, Monte Carlo method was chosen to evaluate the distribution of model output variables, then using multivariate sensitivity analysis methods, though standard regression coefficients to characterize uncertainty in inputs’ impact on model prediction results. Taking Huangling County as an example, applied the optimized ITLUP model to forecast Huanglings’ urban land use and transportation development trend, and explored the impact of models’ uncertainty on the predictions results and the uncertainties’ evolution over time.