# A Microscopic Traffic Flow Data Generation Method Based on an Improved DCGAN
## 1 Introduction
### Literature Review  
In recent years, scholars from various countries have conducted extensive and in-depth research on the field of traffic flow generation. In the early stages of the development of computer science, traffic researchers could only use empirical and mathematical methods for traffic modeling [9]. The data and information required by the TRANSYT system traffic model include the geometric characteristics of the road network, traffic volume data, economic indicators, etc. [PERSON] et al. [10] proposed a new data-driven approach to populate virtual road networks with real traffic flows in 2018. By considering the temporal information of traffic flow as a two-dimensional texture, the generation of new traffic flow can be expressed as a texture synthesis process, which can be solved by minimizing the energy of the newly developed traffic texture. The synthetic output captures the temporal dynamics of the input traffic flow, where vehicle interactions strictly follow traffic rules.  
However, traffic systems are usually complex systems, especially because the characteristics of traffic participants in traffic flow are different [11; 12]. This leads to a high degree of uncertainty and unpredictability of traffic flow data, and it is difficult to accurately describe micro-traffic flow scenes with empirical models or mathematical analysis models. To address this issue, the learning-based approach has brought us to a new way of generating scenarios. Instead of making rules by hand, these models learn actor distributions directly from large datasets, and for any given map, they use regression methods to predict the layout of the actor [13]. Compared with rule-based systems, learning-based systems capture more complex and diverse real-world traffic scenarios. Some scholars [14] utilized a network to generate road macro traffic flow data by using a generative neural network and could well predict the flow distribution of traffic sections. However, macro traffic flow data cannot reflect the movement of surrounding vehicles in vehicle virtual simulation tests, and cannot be directly used in road scene tests [15], and there are few precedents for the implementation of micro traffic flow generation methods that can be used in virtual scene tests.  
With the popularity of AI technology, there are now great advances in the technology of generating data. There are many types of generative neural networks available, and the main types that are often used to generate data are Variational Auto Encoder (VAE) and Generative Adversary Network (GAN) [16]. VAE consists of two parts: the encoder, which converts the training data from high-dimensional features to low-dimensional features based on the statistical distribution; and the decoder, which converts the data into data of the same dimension as the original data based on its low-dimensional features. The VAE learns the distribution features in the original samples and uses them to generate new samples [17]. GAN is composed of a pair of generators and discriminator networks. However, the training of GAN is unstable [18], which may lead to divergent results in the training process. In 2016, [PERSON] et al. put forward the Deep Convolutional Generative Adversarial Network (DCGAN) in their paper [19]. DCGAN uses a Convolutional Neural Network (CNN) to complete the feature extraction of the original data [20; 21] so that the GAN model training process can be more easily divergent.  
The development of generative neural networks has made it feasible to use AI techniques to generate microscopic traffic flow data.