# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 8 Conclusions  
This literature review has examined 100 recent research papers and publications on applying ML algorithms to STSs. It has established the opportunities and gaps for local, state, and federal transportation authorities and professionals to improve transportation performance by using AI technology to fill the gaps and exploit the opportunities. Our findings indicate that 74% of the ML research papers focus on 4 surface transportation application areas, out of a total of 31. The findings reveal that the LSTM algorithm, among the NN and DL learning algorithm categories, and popular classical supervised algorithms (i.e., SVM, random forest, KNN, K-mean, SVR, and fuzzy) are the preferred algorithms for surface transportation problem solving. ML algorithm selection for a specific STS application problem solving essentially is impacted by available expertise and experiences in applying ML algorithms to parallel industries. Therefore, the famous, well-known ML algorithms are also more preferred in solving the STSs application area. Additionally, many STSs applications have a temporal series property. This property makes some ML algorithms such as LSTM with the capability to work with time series data more preferred. On the other hand, STSs application areas with more available data sources are also more preferred for applying ML algorithms. The applied ML algorithms into STSs present supremacy compared to statistical and analytical methods. However, their internal logic is not human interpretable, making it difficult to improve their performance. Generally, DL and MLP algorithms are used extensively in various surface transportation applications resulting from their nonlinear properties and applied experimental history. They prove their capability in forecasting and clustering with an acceptable result while applying limited surface transportation ecosystem input data. Overall, 17 out of 31 well-known surface transportation application areas have not yet been explored using ML algorithms. The absence of data and data integrity in STSs are the primary reasons that impact ML algorithm selection and deployment choices. Although algorithms such as reinforcement learning algorithms can be instrumental in solving some complex transportation system problems, data scarcity and the unavailability of open-source data platforms limit the use of such tools. Spatial behavior and external factors are rarely considered in the research world when considering the factors that impact traffic and transportation systems as the input data when applying ML models. This problem is primarily caused by a lack of reliable, open, integrated, and accessible transportation system databases. The transportation sector must clearly state its problems and needs and develop an open-source, high-quality, high-frequency integrated spatiotemporal dataset for outside knowledge areas. Access to such platforms accelerates ML professionals' transportation problem-solving engagements, given that ML experts have initiated most ML transportation projects. The transportation community has not yet been able to exercise or take advantage of ML techniques for applied projects in their fullest potential. Additionally, there is an ongoing challenge regarding the collection of the large number of traffic data required to process and build valuable information. This vicious circle must be broken by a collaborative partnership such as a public-private partnership. Federal, state, and local authorities, academic research bodies, and private technology provider firms must collaborate on a shared platform to present accurate, integrated, valid, and high-frequency traffic data. Additionally, this collaboration must define the essential goals and problems regarding the research and development domain. The platform can be enriched by applying ML techniques to answer predefined challenges.