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The modern world is full of networks as they impact our lives; the traffic road network is one prominent example. In the last few years there has been considerable interest in the network traffic routing management.
Generally speaking, every network user wishes to have the best service possible at the lowest possible cost. Regulation of traffic in many domains cannot impose such as in “The internet or road networks”, we are concerned in those settings where each user acts according to his /her own selfish interests. Therefore, the networks participants’ actual behavior can be predicted by utilizing the principles of game theory. Exploiting game theory approaches represent one of the most sophisticated solutions to handle various nowadays problems (Allen & Luiz, 2006).
In the same direction, because of the growing number of vehicles is gradually causing congestion issue, routing researches nowadays are moving toward avoiding network-level routing inadequacies by imposing adapters (users) either to utilize routes themselves or to adapt overlay routing networks. It was possible to alleviate routing management which direct route adapters to routes with few traffic flows to gain the shortest travel time. However, the latest route management algorithms pay more attention to one adapter and neglect the adapter coordination which results in significant deterioration of efficiency caused by lack of coordination (D. & J., Reasoning about a highly connected world, 2010).
We suppose that every user will always choose the minimum latency path to its destination and also all users are rational and non-malicious. All these can be viewed as a non-cooperative game where each user plays the best response given the state of all other users, and thus we expect the routes chosen to form Nash equilibrium. (Hyunmyung, Jun-Seok, & R, 2009)
Nash equilibrium usually takes place when all the players take the best reaction to the other's strategic choices jointly. That is, despite what the other player is doing, he or she opts for the right move. Of course, this would be easier if the players knew which strategies their opponents would choose on a timely manner. (Ken, 2008)
A variety of approaches and models based on game theory have been proposed with regard to discovering optimal solutions for network reliability “connectivity” and traffic routing problems. The following is a brief illustration of these approaches and models.
In (Mohammed, Ahmed, & Abdallah, September, 2015), a new automated network routing algorithm has been developed. The developed algorithm replaces the ordinary state-of-the-practice control system by utilizing future automated vehicle capabilities at intersections. The proposed algorithm was inspired by the chicken-game. In the same direction, the study argues that, when Nash’s constraints applied, the participated vehicles follow the Nash equilibrium. The extracted outcomes of the simulated experiments showed that the proposed study achieves a 49% reduction in average travel time delay and an 89% reduction in overall delay when it is compared to the all-way stop sign-controlled intersection.
A level-k game-theoretic framework for modeling traffic presented in (Nan, Ilya, Anouck, & Yildiray, June 2018). The proposed framework models the time-extended, multi-step, and interactive decision making of autonomous vehicles (AV) at uncontrolled intersections. Also, a rule-based approach exploited to measure the conditions that lead to conflicts between vehicles at the intersection. It was shown, through simulations that the proposed modeling traffic framework has the capability to resolve conflicts of different driver types at the intersection.
A distributed cooperative routing algorithm DCR is proposed in (J., J., Q., & B., 2019). The proposed DCR algorithm has been used to overcome misleading coordinate vehicles problem using evolutionary game theory. The proposed method utilized roadside units (RSUs) to evaluates the performance of DCR and measure its effectiveness to balance the traffic flow distribution as well as compute the total travel time. The extracted results show that the total travel time has been reduced to the minimum.