# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 5 Method  
Research and developments regarding ML techniques and applications have exploded in recent years. A simple search on the Scopus [44] database for \"Machine Learning\" terms returns more than 330,000 documents dated between 1959 and 2021; however, the distribution is not normal. For example, although there were 8000 documents in 2014, this surged to 80,000 documents in 2021. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research** & **Goal** & **Result** \\ \hline \multirow{4}{*}{**Machine Learning**} & \multirow{4}{*}{Summarizes the relationship between accuracy and the influenced factors of the DL prediction models by adopting a search strategy, followed by a meta-analysis on prediction accuracy.} & DL models demonstrate better prediction accuracy than conventional ML models; with a 100-million-fold increase in the input data, the prediction accuracy increases by 5.9\% on average. In contrast, the accuracy diminishes by 5.3\% with a 100 min longer prediction horizon. The combined convolutional neural network long short-term memory (CNN-LSTM) has the most significant prediction accuracy, followed by LSTM and deep belief network models. \\ \cline{2-3}  & \multirow{4}{*}{Examines the ML model by comparing implementation difficulty, implementation cost, dataset requirements, DL structure, spatiotemporal features, prediction time costs, maintenance costs, and robustness.} & The review concludes that applying RNNs and CNNs together or convolutional recurrent neural networks (Conv-RNN) with a sequence-to-sequence (Seq2 Seq) structure and attention-based models are popularly applied algorithms. \\ \cline{2-3}  & \multirow{4}{*}{Focuses on applied CNN, such as graph convolutional neural networks and graph attention networks (GATs), to forecast traffic and transportation measures based on a review of 212 papers.} & Provides recommendations for improving the research ecosystem, including: (1) a centralized data repository for GNN-based traffic forecasting resources to facilitate models’ performance comparison and collaboration; (2) GNN fusions with other techniques and modeling approaches to overcome inherent challenges and achieve better performance; (3) applying data augmentation for DL algorithm performance boosting; (4) applying the transfer learning method to traffic prediction problems with a frequent lack of historical data. \\ \cline{2-3}  & \multirow{4}{*}{Focuses on the big data evolution attributes in ITS and categorizes them into smart cards, GPS, video, road site sensors, floating car sensors, wide-area sensors, connected and automated vehicles, passive collection, and other sources.} & The review reports substantial unconsidered remaining challenges in ITS, including (1) inaccurate, incomplete, or unreliable data collection in particular locations or at certain times; (2) privacy issues when collecting personal data; (3) data storage and processing capacity limitations; (4) the absence of an open-access data ecosystem for transportation service providers and app developers to find and re-use the data effectively. \\ \cline{2-3}  & \multirow{4}{*}{Focuses on transportation data-driven methods, such as autonomous vehicles and energy, traffic data analysis and enhancements, travel time estimation accuracy, travel behavior analysis, public transportation data mining, network modeling, and railway system prognostics and health management.} & \multirow{4}{*}{Focuses on transportation data-driven methodologies such as online energy management strategy for plugin hybrid vehicles, NN algorithms for classifying vehicles through a single loop detector, data fusion algorithms for travel behavior predictions, an algorithm applying density-based spatial clustering of applications with a noise algorithm for clustering travelers’ pick up/drop off locations, and public transportation planning using big data approaches. Adopts an example-oriented approach by defining transportation problems and implementing data-driven algorithms as the solution.