Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we choose the proportion of self-driving car to be a variable, denoted by k. Based on the least square method, we find two critical values of k that are 38.63% and 68.26%. When k < 38.63%, the self-driving cars have a negative influence to the traffic. When 38.63% < k < 68.26%, they have a positive influence to the traffic. When k > 68.26%, they have significant improvement to the traffic capacity of the road.

Based on the entropy weight method and the gray relational analysis method, we first calculate the weight of each index and the correlation coefficient between the indicators, get the urban intelligent growth index, and then calculate the annual growth rate of the composite index. We find the following results. First, Suzhou to improve its environment, the success rate of 109.67%. Saint Louis to improve its economy, the growth rate of 57.4%. Second, the sensitivity of the data analysis, each of the indicators is increased by 10%, 20%, 30%, other indicators remain unchanged. Recalculate the city's intelligent growth index, we find that the greater the volatility, the greater the potential. The total population of the city, built green area, the total length of the bus operating a greater potential, built-up area, the smallest regional GDP potential. Finally, we propose an improved model combining remote sensing with GIS to analyze urban expansion and farmland loss from time and space qualitatively and quantitatively.

A model for urban wisdom growth of sustainable development is proposed in this paper. Based on the entropy weight method and the gray relational analysis, we calculate the weight of each index and the correlation coefficient between the indicators, and get the urban intelligent growth indexes andobtain the annual growth rate of the composite indexes.The innovation of this paper is that the entropy weight method and the gray relational analysis method are applied to evaluate the urban intelligence growth index for the first time and calculate the growth rate of the annual comprehensive indexes. The success degree of urban intelligent growth is divided by the annual growth rate divided by the current growth rate.