# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 7 Discussion
Moreover, sophisticated NN algorithms, such as GAN and VAE, are not considered for STS problem solving. This ignorance could be caused due to the algorithms' complexity and novelty. As a result, the algorithms are not simply fitted into STS modeling and are not easily understood by the practitioners. The simulation technique is fundamental for several microscopic and macroscopic traffic and transportation studies. Transportation professionals have widely adopted this method for planning, optimizing, and evaluating transportation projects. The simulation technique attempts to collect transportation-related data, aggregate the data, create a computer-formulated model, and reproduce the transportation system. The outcome is generally not an accurate approximation of the real-world traffic behavior, considering the unpredictability of STSs' uncertainty and interactions. ML algorithms can be utilized to improve the accuracy of transportation simulation and modeling. This utilization is an unexplored field for which we could not find any research papers. The lack of a knowledge base regarding the application of ML algorithms to modeling and simulation techniques is an identified research gap. On the other hand, the GAN and VAE are robust algorithms that collect real-world input data, recognize their patterns, and reproduce scenarios similar to those in the real-world data. For example, these algorithms prove their power in creating virtual photographs by viewing real-world pictures. Alternatively, they can write a short story by viewing several human-written novels. These examples have concepts that are very similar to simulations. Thus, there is an opportunity to feed vast real-time traffic data into the GAN and VAE algorithms to produce similar scenarios. These reproduction instances could represent the same concept as a simulation but with a data-driven approach. Although ML algorithms can use external factors other than traffic data to implement an STS model, most applied ML models use internal factors, such as speed, flow, and capacity. For example, the models could adapt the weather conditions, time of day, accidents, regional economic data, and other external factors to be used as the input data and examine these factors to develop a more sophisticated model. On the other hand, there is serious concern about the computational intensity of ML algorithms, specifically DL algorithms. DL algorithms with several layers are seriously computationally and memory intensive depending on the number of features and the number of input data. This high computational cost severely limits DL algorithms' application. However, the market answers the intensifying demand for higher processing power. For instance, big data analysis requirements, such as real-time video processing for AVs, force the technology providers to adopt cheaper processing power and memories. The processors' advancements remove the barriers to applying DL methods such as GNN, VAE, LSTM, and RNN in STSs more efficiently and include spatiotemporal and external factors. Hence, a well-established STS database presentation with external and internal characteristics could be a potential platform. The platform would inspire data scientists to apply ML when implementing more sophisticated STS models.