# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 7 Discussion
Therefore, the surface transportation network dataset must be well presented to link the mobility data to an integrated network. The most applied ML algorithms do not use spatial data as an input and focus only on the time-series dimension. Although ignoring the STS spatial data simplifies the problem, there are ML algorithms that can take the spatial data into account for resolving more complex STS problems. In the absence of spatial data, ML algorithms can still serve as valuable prediction tools compared with statistical and heuristic methods. Several STS applications use MLP and DL algorithms. This popularity results from the algorithm's ability to adapt to a specific problem's context by tuning the number of layers for feature extraction. Some variations of NN algorithms, such as CNNs, account for the surface transportation spatial property. Although CNNs improve the surface transportation network presentation in ML algorithms, it depends on the Euclidian distance, which is not a valid transport network interpretation. For instance, two parallel roads can be near each other without any network relation, but CNNs consider them related. Recent attempts employ more sophisticated techniques to combine the temporal and the network presentation with the ML algorithm to improve the STS modeling. The most popular one is GNN, which exploits NNs' capabilities alongside network presentation techniques. GNN has a unique ability to apply STS network features with time-series properties to develop a spatiotemporal network for node, edge, feature, pattern forecasting, clustering, recognition, and regeneration. The primary constraint with sophisticated ML algorithms is the lack of publicly available spatial data. As a result, researchers typically employ OpenStreetMap [142] as a base roadway network and add other surface transportation network properties to the base layer. However, OpenStreetMap adaptation can be challenging, time-consuming, and inaccurate. Such hurdles are caused by the diverse scales and georeferenced data \begin{table}
\begin{tabular}{c c c} \hline \hline
**STS Application Area** & **ML Algorithm** & **Number of Papers** \\ \hline Speed/capacity/flow/travel time forecasting & LSTM & 9 \\ Speed/capacity/flow/travel time forecasting & GNN & 8 \\ Speed/capacity/flow/travel time forecasting & SVM & 7 \\ Speed/capacity/flow/travel time forecasting & KNN & 6 \\ Speed/capacity/flow/travel time forecasting & GRU & 6 \\ Speed/capacity/flow/travel time forecasting & MLP & 6 \\ Speed/capacity/flow/travel time forecasting & Fuzzy & 4 \\ Crash/accident/incident/congestion forecasting & Random forest & 4 \\ Crash/accident/incident/congestion forecasting & XGBost & 3 \\ Demand/mode forecasting & MLP & 3 \\ Demand/mode forecasting & Random forest & 3 \\ Demand/mode forecasting & DCNN & 3 \\ Demand/mode forecasting & XGBost & 3 \\ Speed/capacity/flow/travel time forecasting & Linear regression & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Most utilized ML algorithms in STS application areas. standards involved in unique map transformations. For instance, a bus network, including the trajectory, bus stops, terminals, entrance/exit port, ticketing stations, and real-time bus movements, is all georeferenced data that must be adapted to road networks. Moreover, the adjusted data must be integrated into infrastructure components (i.e., walkways, bikeways, subway tunnels, and stations) and traffic components (i.e., traffic signals, on-street parking, and land use). Due to its crowdsourcing nature, OpenStreetMap is not a consistent or reliable network platform. Therefore, the absence of a publicly available, integrated, reliable, and accurate georeferenced transportation system database is a fundamental, required step for the use of spatiotemporal data in the application of ML algorithms. As a result, many robust ML algorithms, such as reinforcement learning algorithms and unsupervised learning algorithms, are hardly used in STS problem solving.