# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 6 Results  
We conducted a comprehensive review of more than 100 recently peer-reviewed papers (95% published after 2018) in the area of ML algorithm applications in STSs. The ML algorithms' application popularity in the reviewed articles is ranked and presented in Table 2. The most applied ML algorithm was MLP, followed by LSTM. Supervised and unsupervised classical learning algorithms, including random forest, SVM, KNN, fuzzy, and linear regression, follow the MLP and LSTM algorithms in terms of usage. The high usage is perhaps due to their simple hyperparameter definitions and their ability to be used in regression and clustering. Furthermore, the algorithms' modeling initiation is minimal, and these algorithms do not require input data graph representation. Instead, the algorithms attempt to determine the input data's internal relationship and make a prediction based on the recognized patterns. These capabilities make the algorithms easy to use and powerful for predicting transportation parameters, such as speed, travel  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ML** & **Number of** & **Papers Using** \\
**Algorithm** & **Papers** & **ML Algorithm** \\ \hline MLP & 19 & [11, 12, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64] \\ LSTM & 16 & [52, 65, 66, 67, 68, 69, 70,  
time, demand, mode choice, collision, parking demand, and operation cost optimization. The factors are local and quantitative and follow the demand/supply equilibrium concept. XGBoost, an ensemble learning algorithm, is a popularly applied algorithm for forecasting and clustering. Researchers have generally reported that XGBoost has better accuracy and performance than similar practices. XGBoost has proven its ability in other application fields and presents the same capability in traffic and transportation systems forecasting and clustering problem solving. According to the reviewed papers, most STS ML papers focus on forecasting and predictions (74%), followed by optimization (11%). Service advancements, ITS technology advancements, and automated vehicles with technology advancements between levels 0 and 5 account for 15%, as illustrated in Figure 11. Some papers applied an ML algorithm to more than one application; therefore, they were counted for each application sector separately. The distribution is given for each application area separately in Table 3. Some areas have not been explored by ML, such as autonomous vehicles with technology advancements between level 0 and 5 (AV L0-L5) that focus on the applications planned for future vehicles. This could be related to a limitation explained in the conclusion section. Likewise, ITS technology advancements are also less focused upon, and applications such as simulations, with all their potential and requirements, are disregarded in ML applications. The forecasting and prediction area, with 69 articles, was found to be the most popular ML application in the STS subject area. Optimization, as part of analytical methods, specifically in operation research [130], has been well practiced by applying ML algorithms with non-linearity analysis capabilities [131]. However, optimization, as the second-ranked group by nine reviewed papers, is far from the first rank. The reviewed papers in the ITS technology advancements application area subcategories were limited to four, and all employ external factors as inputs to the model. Figure 11: Number of papers in various applied research areas. The papers were also categorized based on network behavior and external factors (spatial-external) as inputs to the model. The results are presented in Table 4. Although the forecasting traffic-related factors are ultimately impacted by external factors such as weather conditions, many reviewed articles do not consider external factors as inputs to their model. The reviewed papers regarding ITS technology advancements were limited to four, and all employ external factors as their model inputs.