# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 6 Results
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Category** & **Number of Papers** & **Papers** \\ \hline \multirow{7}{*}{Category} & Demand/mode/OD & 12 & [11,12,53,55,69,78,82,87,94,99,100,105,123] \\  & Driver behavior/speed/ & 39 & [9,50,52,54,60,61,65,66,68,72-75,77,83,88- \\  & Capacity/flow/travel time & 90,92,93,96,106,108-118,120,121,127,129,132] \\  & Crash/accident/incident/conge & 13 & [10,48,51,70,71,80,81,84,85,97,98,102,124] \\  & Arrival time & 1 & [57] \\  & Weather condition & 2 & [51,89] \\  & Air quality & 2 & [64,125] \\ \hline \multirow{7}{*}{Category} & Demand/supply & 1 & [122] \\  & Schedule & 1 & [95] \\  & Network & 1 & [50] \\  & Fuel/energy & 2 & [49,63] \\  & User cost & 1 & [79] \\  & Operation cost & 4 & [59,79,86,104] \\ \hline \multirow{7}{*}{Category} & Automatic toll collection & - & - \\  & Dynamic congestion pricing & - & - \\  & Automatic fare collection & - & - \\  & Parking management & 2 & [67,91] \\  & Classification/detection/countu & 2 & [101,103] \\ \cline{2-4}  & Network integration & 3 & [51,107,128] \\ \hline \multirow{7}{*}{Category} & Adaptive traffic signal control & 3 & [50,62,126] \\  & Opposite driving detection & - & - \\  & Over-speeding detection & - & - \\  & Automatic license plate & 1 & [56] \\  & recognition & - & - \\ \cline{2-4}  & Red-light passing detection & - & - \\ \cline{2-4}  & Simulation & - & - \\ \cline{2-4}  & Dynamic routing & - & - \\  & Location-based services & - & - \\  & Lane change detection & 1 & [76] \\  & Collision detection & 1 & [119] \\  & Adaptive cruise control & - & - \\  & Automatic parking & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Research papers’ distribution in STS application areas. The following presents some of the reviewed papers and their highlighted methods and outputs as a sample. The work in [87] is one of the few research articles applying network behavior and external factors as inputs. It focuses on the public transportation network's spatial property with a 30 min temporal interval. It applied k-fold crossing validation and decision tree combination with bagging and boosting to forecast ridership. Moreover, it evaluated various external measures' impact factors on ridership. The works in [99; 100] applied CNN and DL for passenger face detection to improve passenger-counting system performance. Although this ML application did not directly address transportation system factors, it helped improve traffic performance. The work in [9] applied GNN and GRU for a real-time mode choice prediction and reported a maximum accuracy of R\({}^{2}\) = 0.95. The work in [132] applied spatiotemporal data for short- and long-term traffic prediction by defining a spatiotemporal network. The model consisted of several layers of spatiotemporal blocks, with a multi-resolution temporal module and a global correlated spatial module. The model was arranged concurrently in sequence to extract the dynamic temporal dependencies and the global spatial correlations. The work in [111] implemented a traffic scene as a graph of interacting vehicles in a flexible abstract representation and applied GNN models for traffic prediction. It reported that the prediction error in scenarios with considered interaction decreased by 30% compared to a model without considered interactions. The work in [77] proposes a DL model that concurrently extracts the spectral traffic features using a graph CNN. Additionally, it considered the temporal features by applying LSTM to predict travel time. The model transfers the fixed-length-interval GPS trajectory data to a formatted adjacent matrix as the graph CNN input. The model was applied to taxi service data and reported an acceptable accuracy compared to a shallow model such as LSTM. The work in [51] reports on the open-source Pikalert [133] system, which combines weather information and real-time data from connected vehicles to provide crucial information to improve STS safety and efficiency.