# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 5 Method
A significant number of documents were articles and conference papers. The document distribution based on their type is presented in Figure 7a. The yearly document distribution is presented in Figure 7b. The distribution shows that most of the documents were primarily published recently, since 2014. We applied a customized systematic literature review framework to capture reliable and diverse outcomes. The framework identified the review purpose, the need for revisions, and review strategy development. The process was a tailored adaptation of [PERSON], [PERSON] et al. (2015), illustrated in Figure 8 and explained thereafter. The intent of Step 1 was to explore specific review questions. The review questions were finalized as follows:  
* Which STS application areas can use ML algorithms? * Which ML algorithm(s) would be most suitable for a particular STS problem? * Can external factors (i.e., weather conditions) be considered as input data? * Can spatial factors be considered as input data? * Which ML algorithm properties make a particular algorithm suitable for a specific STS problem? * Why are some ML algorithms not utilized within the STS domain? * How can the surface transportation sector further exploit ML algorithms? Figure 7: Published documents within ML and surface transportation distribution: (**a**) documents’ distribution by type; (**b**) the number of documents by year. In Step 2, the existing literature reviews and books that address the identified questions in Step 1, either fully or partially, are explored and evaluated. The existing reviews were used to identify and brainstorm the methods, approaches, and results from other studies within the STS domain. This step has already been showcased in the \"Existing Literature Review\" section of this article. In Step 3, a comprehensive web search was conducted through the Web of Science [46] and Google Scholar [47] databases to collect literature in the related fields. In Step 4, we searched for articles using the logical \"and\" and \"or\" format. By conducting this search, more than 400 papers were found. The keywords used for our search were terms such as follows:  
* 'road transportation' AND ('machine learning' OR 'ML' OR 'LSTM' OR 'neural network');
*'surface transportation' AND ('machine learning' OR 'ML');
* 'traffic management' AND ('machine learning' OR 'ML');
* 'road traffic' AND ('machine learning' OR 'ML');
* 'road accident' AND ('machine learning' OR 'ML' OR 'LSTM' OR 'neural network');
* 'traffic prediction' AND ('machine learning' OR 'ML' OR 'LSTM' OR 'neural network'). In Step 5, duplicates and potentially non-applicable articles were eliminated. In Step 6, we reviewed the papers' abstracts and dismissed papers that were not explicitly related to the STS and ML algorithms. In total, 140 papers remained after this step. In Step 7, the full manuscripts of the 140 papers were downloaded, and fields such as the publication year, web source link, title, abstract, methodology, summary of results and findings, applied ML algorithms, considered transportation areas, and the publication of each paper were extracted to build a database. In Step 8, papers deemed irrelevant or outside of this study's scope were likewise eliminated. Moreover, if the paper was specifically a review paper, it was tagged separately for further referencing. In Step 9, the collected papers' citations were considered by applying the snowballing concept, particularly papers with the highest citations added to the database if they were not already added. In Step 10, a graph structure was created for STS applications and linked to each STS ecosystem component. The graph type is a many-to-many graph, in which each STS component can be connected to multiple applications (i.e., the rail from STS components is related to dynamic routing and location-based services  
Figure 8: Literature review process steps.