## 2 Literature Review and Conceptual Context
### Situation-Aware Route Planning
#### 2.2.1 Routing Using Traffic Conditions  
Vehicle routing using traffic can be classified by the usage of the data: real-time usage, prediction usage, or hybrid. Moreover, this data can come from experienced drivers such as taxicabs or sensors on specific roads. Two studies used the Ant Colony Algorithm (ACO) to find the best possible routes. The first one started its framework by predicting traffic and using real-time data only for non-recurrent heavy congestion [48]. However, the principal objective was not to look for a set of routes but to use ACO and jam time windows for each road to make updates to the route every time the car was positioned in a junction node of the traffic network. The second study used a set of three dynamic network graphs: distance, speed, and travel time, when considering traffic [47]. It used ACO for vehicle routing, and it updated the routes with the depreciation of travel time according to the dynamic traffic graph. A common method to find the best paths is the Dijkstra algorithm. A hybrid model predicted short-term near-future traffic and minimal real-time information to feed the Dijkstra algorithm, outperforming static and dynamic routing [45]. A real-time usage model performed two different methods. The first one was based on the traffic flow propagation in the network using spatiotemporal correlation. In contrast, the second was based on the time-varied spare flow capacity of the road link in question [43]. This model updates traffic flow input data every time interval, showing that proactive route guidance leads to fewer re-routings for each rerouted vehicle. A more advanced hybrid model using another simple routing algorithm, k-shortest paths, selects a pre-set pool of possible and optimal routings and predicts future traffic states with LSTM NN. This approach does not require a constant flow of real-time information [53]. However, it requires probe cars to detect non-recurrent congestion and uses this information to eliminate K-shortest paths that contain congested roads. Another study used LSTM to predict traffic but incorporated a double-rewarded value iteration network (VIN) to make the routes [55]. The VIN was fed the traffic prediction and data from a taxicab driver dataset, incorporating experience and knowledge of traffic trends. The proposed model achieved human-like responses, but for optimal vehicle routing, a human-like response is not optimal given the dynamic variables. Finally, a deep learning framework used Deep Belief Neural Network Classifier and historical experience data merged with weather variables to identify traffic congestion features and make a route selection [51]. Other types of machine learning tool can be used for traffic rerouting, such as DBSCAN, a density-based type of clustering. The framework that has used this tool has three steps: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Reference** & **Variables** & **Methods** \\ \hline
2014 & [43] & Traffic & Dijkstra \\ \hline
2014 & [44] & Traffic, Weather & RPSA, ABM \\ \hline
2016 & [45] & Traffic & Dijkstra \\ \hline
2016 & [46] & Weather & RSA, CEPO \\ \hline
2017 & [47] & Traffic & Ant Colony, Dynamic Graph \\ \hline
2017 & [48] & Traffic & Ant Colony \\ \hline
2018 & [49] & Traffic & Floyd’s, DBSCAN \\ \hline
2018 & [50] & Weather & RSA, CEPO \\ \hline
2018 & [51] & Traffic, weather & DB NN \\ \hline
2019 & [52] & Traffic, Weather, Distance & Autoencoder NN \\ \hline
2019 & [53] & Traffic & k-shortest path, LSTM NN \\ \hline
2019 & [54] & Traffic & Heuristic search A* algorithm \\ \hline
2019 & [55] & Traffic & LSTM NN \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Situation-aware routing planning articles and characteristics. (1) differentiate populated roads from non-populated roads, (2) assign weights to routes based on the traffic intensity at a given point in time, and (3) suggest routes based on user's input [49]. This framework develops a traffic density factor using sensor data and previous traffic density factors.