# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 6 Results
The paper concludes that the Pikalert developmental framework offers essential environmental information required to expand and develop CAV. The work in [64] applied ANN models to capture the spatial distribution of particulate matter and black carbon emissions and compared the results to land use regression (LUR) methods that were extensively employed. The paper reports a superior performance for ML algorithms compared with the LUR model. The work in [57] proposes the application of the wavelet neural network (WNN) [134] and the intelligent-particle swarm optimization (IPSO) algorithm [135] (replacement for gradian decent method) to \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Network Behavior**} & \multicolumn{2}{c}{**External Factors**} \\ \cline{3-4}  & & & **Yes** & **No** \\ \hline \multirow{3}{*}{Forecasting} & \multirow{3}{*}{Yes} & [9; 51; 64; 65; 82; 87; 89; 93; 114; 125; 127] & [53; 77; 109; 111; 113; 120; 132] \\ \cline{2-4}  & & & [12; 48; 50; 51; 53; 54; 55; 69; 70; 71; 72; 80; 84; 85; 90; 97; 100; 102; 118; 121; 123; 124] \\ \cline{2-4}  & & & [12; 48; 50; 51; 53; 55; 69; 72; 80; 84; 85; 90; 97; 100; 102; 118; 121; 123; 124] \\ \hline \multirow{2}{*}{Optimization} & \multirow{2}{*}{Yes} & [49; 50; 59; 63; 86; 95] & - \\ \cline{2-4}  & & No & [79; 104; 122] & - \\ \hline \multirow{2}{*}{Service advancements} & \multirow{2}{*}{Yes} & [51; 91; 101; 107; 128] & - \\ \cline{2-4}  & & No & [67; 103] & - \\ \hline ITS technology & \multirow{2}{*}{Yes} & [50; 126] & - \\ advancements & & No & [56; 62] & - \\ \hline AV Lo-L5 technology & \multirow{2}{*}{Yes} & [58; 76; 119] & - \\ advancements & & No & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: STS application areas (spatial–external matrix). predict bus arrival time. A 49% maximum relative error reduction with the IPSO algorithm, compared with the shallow implementation of WNN, was reported. The work in [50] is one of the oldest studies applying NN analysis for physical and behavioral transportation planning problems. The paper concentrates on socioeconomic and demographic factors that impact travel demand patterns by focusing on behavioral properties. Additionally, the study examined the physical aspect of traffic management in a typical intersection by applying various NN algorithms. The paper reports that NN algorithms have a more impressive result for physical applications than for behavioral ones because of deficiencies in behavioral data. The work in [49] applied explainable AI methods to forecast household transportation energy consumption based on household travel survey urban zonal data. The paper reports an 83.4% prediction validation accuracy. Furthermore, the paper reports that although household travel time, mode, and frequency are influential features in transportation energy consumption, the proportional impact of those features varies throughout different zones. The work in [122] presents a hybrid approach of combining order packing by solving a three-dimensional loading problem. This approach used an ML algorithm and a routing method by applying the genetic algorithm in intermodal network optimization. The paper compared the ML algorithms' performance to the performance of ordinary operation research approaches. As a traffic and demand management strategy, parking planning and management solely depend on demand estimation. The works in [67; 91] are two experimental parking demand forecasting studies that used ML algorithms and traffic and network data. Both papers report an acceptable accuracy in terms of parking demand prediction. The work in [101] presents a framework of recurrent convolutional networks (R-CNNs) for car detection. It applied unmanned aerial vehicles to capture images over signalized intersections. The paper reports that the model was insensitive to detection load with acceptable accuracy.