Since the reform and opening up, Chinese economy has developed rapidly, and people's living standards have been greatly improved. Chinese GDP in 2019 is 270 times of that in 1978 and per capita GDP in 2019 is 180 times of that in 1978. The income has increased a lot during the past 40 years, especially in metropolis like Beijing, Shanghai, Guangzhou and Shenzhen. These cities have attracted a lot of investment, providing a large amount of job opportunities, so the population of these cities expands rapidly as well as the scale of the cities. The number of civil automobile in 2019 is almost 5 times of that in 2000 and the travel distance is almost 3 times of that in 2000. Therefore, traffic congestion has become more and more serious in recent years. It takes more than twice as much time to travel during rush hours. The research object is to find out measures to control traffic congestion in Chinese cities.
In this paper, system dynamic is applied to study the transportation system which is also known as the policy laboratory. According to system dynamics, it is important to understand that the system is dynamic and the change of one variable can influence the performance of the whole system. In this research, system dynamics is used to establish a simplified model to reflect the real transportation system. By adjusting the values of the variables, the effect of different policies can be simulated, and the efficiency of the policy can be evaluated.
Firstly, this paper analyzes the traffic development process at home and abroad, summarizes the development characteristics of each stage, and summarizes different measures of congestion control. Secondly, the paper analyzes the traffic operation characteristics of Chinese cities and the causes of road network congestion. Traffic congestion in China mainly includes external factors and internal factors. The external factors refer to the growth of traffic demand caused by economic development, population growth and urban expansion. The internal factors refer to the poor management of the transportation system. Thirdly, based on the causality relationship between different factors, the system dynamic model with virous policies is established. Due to the fact that most of the developed cities located in the southeast China and Shanghai is the largest city in the south, it is selected to be the example of this research. In the end, specific measures are put forward based on the simulation result of the model.

Nighttime light remote sensing data is regarded as an important data that can be used to measure human socioeconomic activities. Because of the positive correlation between long time-series nighttime light data and economic development, long time-series nighttime light data can be used to construct GDP forecasting models to predict GDP. However, there are different temporal resolutions, measurement standards, and errors due to different sources of nighttime light data. How to eliminate the data errors and the differences between different data sources to construct a reliable long time-series nighttime light data is currently a major problem. In addition, different GDP forecasting models have different applicable conditions and forecasting accuracy. How to choose the optimal GDP forecasting model is another major problem. In this paper, data errors and differences are dealt with at first to obtain long time-series nighttime light data that can be quantitatively analyzed. Then, the advantages and disadvantages of different GDP forecasting models are analyzed and compared. The main contents of this paper are as follows.