# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 7 Discussion  
As illustrated in the previous sections, the distribution of STS applications in the reviewed research papers with applied ML algorithms is not uniform. Some specific ML algorithms are used more frequently than others. We suggest a framework based on a customized problem-solving framework to examine the causation of selecting a particular ML algorithm to solve an STS application problem. According to the adapted [PERSON] tetrahedral model of memory experiments, problem solving depends on four interacting parameters: 'problem domain', 'problem-solving goals', 'general thinking and problem-solving skills', and'specialized knowledge and skills' [138]. However, 'general thinking and problem-solving skills' influence the ability to apply'specialized knowledge and skills' to solve problems. Therefore, problem-solving strategy selection depends on the problem definition as domain specificity. Additionally, problem-solving goals are driven by data availability, the required outcome results, and expertise as'specialized knowledge and skills'. Therefore, the framework could be simplified to a three-pillar problem-solving strategy selection criteria context, as shown in Figure 12. We used the framed context for ML technique selection for application in STS problem solving. Selecting a suitable ML algorithm for a particular application depends on these three main criteria. Our research shows that a limited set of ML algorithms are used for a group of narrow STS applications. Table 5 presents the most frequent ML algorithms utilized for STS application areas. It demonstrates that LSTM (the most prevalent RNN algorithm) and popular classical supervised algorithms (e.g., SVM, random forest, KNN, k-Mean, SVR, and fuzzy) are the most preferred ML algorithms for prediction in surface transportation. Such popularity might be due to the ML algorithms' primary nature, their extensive utilization for forecasting purposes. Additionally, data scientists are intimately familiar with the implementation of a digital model of real-world problems to estimate some system behavior as a typical ML problem-solving method. Several internal and external factors impact transportation systems. These impacts have been examined only by measuring influencing factors such as mode choice, demand, traffic volume, speed, and flow changes. For example, although a rainy day affects the mode choice of people, demand prediction models rarely consider weather conditions as the input and instead focus on influencing factors only. Furthermore, the traffic measures such as speed, flow, and demand are time-series data. Therefore, the use of the RNNs and LSTM primarily for STSs' forecasting objectives is practical. Figure 12: ML algorithm selection criteria context. Moreover, many practices employ RNN algorithms in other time-series prediction knowledge areas. Traffic-related data with time-series contexts are likewise appropriate for the use of the RNN algorithm. Additionally, the accessibility of data resources in the STS context is crucial. Few publicly available data sources are offered for STSs, such as the performance measurement system [139]. This dataset contains speed and volume data in five-minute intervals from 18,000 various vehicle detectors on highways of California between 2001 and 2019. Other trajectory data, such as taxi and ride-hailing data, are rarely available [140; 141]. A few open public traffic data sources for ML applications are presented in [42]. Therefore, according to the ML three-decision-criteria context, the publicly available STS data, the lack of expertise in spatiotemporal ML modeling, and the absence of STS problem definition presentation are obstacles to the application of ML algorithms for the enhancement of STS application areas. A simple time-series prediction problem is the most straightforward fit for ML algorithms and is hence the most used. However, mobility data have spatial dimensions that create a spatiotemporal context for STSs.