# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Protocol**} & \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Recovery Strategy**} & \multirow{2}{*}{**Digit Map Required**} & \multicolumn{2}{c}{**Traffic Information System Required**} \\  & & & & & \\ \hline
2000 & GPSR & Ad Hoc & Perimeter & No & No \\
2005 & GPCR & City & Carry and Forward & Yes & No \\
2007 & GyTAR & City & Carry and Forward & Yes & Yes \\
2011 & E-GyTAR & City & Carry and Forward & Yes & Yes \\
2011 & HTAR & City & Carry and Forward & Yes & No \\
2012 & RIVER & City & Carry and Forward & Yes & No \\
2013 & JTAR & City & Carry and Forward & Yes & No \\
2013 & iCAR & City & Carry and Forward & Yes & No \\
2014 & TFOR & City & Carry and Forward & Yes & Yes \\
2015 & ARP-QD & City & Carry and Forward & Yes & Yes \\
2016 & AQRV & City & Carry and Forward & Yes & No \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geographic routing protocols, GPSR: Greedy Perimeter Stateless Routing: GPCR: Greedy Perimeter Coordinator Routing; GyTAR: improved Greedy Traffic Aware Routing; E-GyTAR: Enhanced GyTAR; HTAR: Hybrid Traffic-Aware Routing; RIVER: Reliable Inter-Vehicular Routing; JTAR: Junction-based Traffic-Aware Routing; iCAR: Intersection-based Connectivity Aware Routing; TFOR: Traffic Flow-Oriented Routing; ARP-QD: Adaptive Routing Protocol based on QoS and vehicular Density; AQRV: Adaptive QoS based Routing for VANETs. Figure 2: Infrastructure-Free Traffic Information System (IFITS) in GyTAR. (AQRV) [26] are based on the same idea of dynamic junction selection as GyTAR. E-GyTAR and TFOR further consider the flow directions of the roads. Selecting a junction with a high density, but in the opposite direction to the destination, might still incur a high delay. In E-GyTAR, data packets are forwarded on the road with a high traffic flow towards the destination. Alternatively, TFOR considers both the directional and the undirectional flow of traffic density. If traffic flow is in the opposite direction but the density is high enough, the junction can still be selected as a candidate. In iCAR, roads with the highest vehicular density are not necessarily be selected as forwarding paths, in order to avoid a high transmission delay due to a high data volume. The next-junction selection is based on the real-time traffic density, the delay information, and the estimated connectivity lifetime of roads. A distributed scheme, namely Road Segment Evaluation (RSE), is proposed to evaluate the suitability of road segments. On the other side, ARP-QD considers QoS requirements of different applications. It balances the path efficiency and path stability by selecting a forwarding path based on hop count and link duration. In AQRV, QoS constraints, in terms of connectivity probability, packet delivery ratio, and delay, should be satisfied in the intersection selections. This problem is modeled as a multi-objective optimization problem and solved by ant colony optimization. When selecting the next intersection, possible intersections are explored by forward ants, and then the routing decision is made based on the corresponding backward ants. However, the above approaches only consider the next junction. If the following road segments do not have sufficient vehicular density to support data forwarding, high latency still cannot be avoided. Reliable Inter-Vehicular Routing (RIVER) [19] is a geographic routing protocol with a monitoring mechanism to evaluate the reliability of roads. The main monitoring mechanism proposed in this paper includes two parts: active monitoring and passive monitoring. In active traffic monitoring, a probe message is sent along a specific road segment by a vehicle near the edge of the road. If the probe message can reach another edge, a responsive message will return to the source vehicle, which indicates this road has sufficient traffic density. This knowledge is then used in route selection. Another passive traffic monitoring is proposed for knowledge sharing to reduce the overhead.