## 2 Literature Review and Conceptual Context
### Situation-Aware Route Planning  
Situation-aware route planning entails more variables than just the vehicles, the starting position, and the destination. It is usually studied for problems that include the vehicle's initial position and destination. Still, it could be applied at each delivery during the last mile to improve the estimated arrival time [41]. Dynamic, sometimes stochastic, variables such as traffic and weather come into play. However, many data collection methods are unreliable and can lead to high sparsity [42].  
On the other hand, using these datasets to route vehicles in real time creates agglomeration on previously empty roads. This section presents several studies that have investigated dynamic routing phenomena. A summary of these studies can be found in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Reference** & **Variables** & **Prediction Type** & **Area** & **Method** & **Traffic Data** \\ \hline
2012 & [19] & Weather & Regression & Freeway & KNN & Floating car \\ \hline
2014 & [38] & Weather, social media & Regression & Highway & Bayesian Networks & Sensor \\ \hline
2014 & [28] & Weather & Regression & Highway &
\begin{tabular}{c} MLP NN, \\ Mutual information \\ \end{tabular} & Sensor \\ \hline
2015 & [20] & Weather & Regression & Freeway & KNN & Sensor \\ \hline
2015 & [26] & Weather & Regression & Freeway & SVR & Sensor \\ \hline
2015 & [30] & Weather & Regression & Urban & GMM, ANN & Sensor \\ \hline
2016 & [37] & Weather, social media & Regression & Freeway & Deep Belief NN & Sensor \\ \hline
2016 & [25] & Weather & Classification & Highway & Bayesian Networks & Sensor \\ \hline
2016 & [32] & Weather & Classification & Freeway & LSTM NN & Sensor \\ \hline
2016 & [35] & Social media & Regression & Highway & custom algor. & Sensor \\ \hline
2017 & [21] & Weather & Classification & - & Random Forest & Sensor \\ \hline
2017 & [31] & Weather & Regression & Highway &
\begin{tabular}{c} Deep Belief NN, \\ LSTM NN \\ \end{tabular} & Sensor \\ \hline
2017 & [29] & Weather & Regression & Freeway & GRU NN & Sensor \\ \hline
2017 & [15] & - & Regression & Urban/highway & LSTM NN & Sensor \\ \hline
2018 & [27] & Weather & Regression & Highway & Cascaded NN & Sensor \\ \hline
2018 & [39] & Social media & Regression & Urban & GCN NN, LSTM NN & Sensor \\ \hline
2018 & [36] & Social media & - & Urban & custom algor. & Sensor \\ \hline
2019 & [24] & Weather & Regression & Urban &
\begin{tabular}{c} Recursive Least Square, \\ Kalman Filter \\ \end{tabular} & Floating car \\ \hline
2019 & [11] & - & Regression & Highway & LSTM NN & Floating car \\ \hline
2019 & [13] & - & Regression & Highway & MLP NN & Sensor \\ \hline
2019 & [14] & - & Regression & Highway & Lasso, GBRT & Sensor \\ \hline
2019 & [34] & Weather & Regression & Urban & GC NN, LSTM NN & Floating car \\ \hline
2019 & [22] & Weather & Regression & Highway &
\begin{tabular}{c} Lasso, \\ Random Forest, SVR \\ \end{tabular} & Sensor \\ \hline
2019 & [33] & Weather & Regression & Freeway & LSTM NN & Sensor \\ \hline
2020 & [16] & - & Regression & Urban/highway & LSTM NN & Sensor \\ \hline
2020 & [18] & - & Regression & Freeway & GCN NN, LSTM NN & Sensor \\ \hline
2020 & [17] & - & Regression & Urban/highway & GNN & Sensor \\ \hline
2020 & [10] & - & Classification & Highway & LSTM NN & Sensor \\ \hline
2022 & [12] & - & Regression & Freeway & LSTM NN & Sensor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Traffic prediction articles and characteristics.